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Home/ Featured/Data Mining and Data Warehouse Solved MCQs

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On: July 4, 2023 Posted in Featured Comments: 0 Views: 70

Data Mining and Data Warehouse Solved MCQs

1.__________ is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions.
A.data mining.
B.data warehousing.
C.web mining.
D.text mining.
Answer» B. data warehousing.

2.The data Warehouse is__________.
A.read only.
B.write only.
C.read write only.
D.none.
Answer» A. read only.

3.Expansion for DSS in DW is__________.
A.decision support system.
B.decision single system.
C.data storable system.
D.data support system.
Answer» A. decision support system.

4.The important aspect of the data warehouse environment is that data found within the data warehouse is___________.
A.subject-oriented.
B.time-variant.
C.integrated.
D.all of the above.
Answer» D. all of the above.

5.The time horizon in Data warehouse is usually __________.
A.1-2 years.
B.3-4years.
C.5-6 years.
D.5-10 years.
Answer» D. 5-10 years.

6.The data is stored, retrieved & updated in ____________.
A.olap.
B.oltp.
C.smtp.
D.ftp.
Answer» B. oltp.

7.__________describes the data contained in the data warehouse.
A.relational data.
B.operational data.
C.metadata.
D.informational data.
Answer» C. metadata.

 (2)

8.____________predicts future trends & behaviors, allowing business managers to make proactive, knowledge-driven decisions.
A.data warehouse.
B.data mining.
C.datamarts.
D.metadata.
Answer» B. data mining.

9.__________ is the heart of the warehouse.
A.data mining database servers.
B.data warehouse database servers.
C.data mart database servers.
D.relational data base servers.
Answer» B. data warehouse database servers.

10.________________ is the specialized data warehouse database.
A.oracle.
B.dbz.
C.informix.
D.redbrick.
Answer» D. redbrick.

11.________________defines the structure of the data held in operational databases and used by operational applications.
A.user-level metadata.
B.data warehouse metadata.
C.operational metadata.
D.data mining metadata.
Answer» C. operational metadata.

12.________________ is held in the catalog of the warehouse database system.
A.application level metadata.
B.algorithmic level metadata.
C.departmental level metadata.
D.core warehouse metadata.
Answer» B. algorithmic level metadata.

13._________maps the core warehouse metadata to business concepts, familiar and useful to end users.
A.application level metadata.
B.user level metadata.
C.enduser level metadata.
D.core level metadata.
Answer» A. application level metadata.

14.______consists of formal definitions, such as a COBOL layout or a database schema.
A.classical metadata.
B.transformation metadata.
C.historical metadata.
D.structural metadata.
Answer» A. classical metadata.

15._____________consists of information in the enterprise that is not in classical form.
A.mushy metadata.
B.differential metadata.
C.data warehouse.
D.data mining.
Answer» A. mushy metadata.

16.. ______________databases are owned by particular departments or business groups.
A.informational.
B.operational.
C.both informational and operational.
D.flat.
Answer» B. operational.

17.The star schema is composed of __________ fact table.
A.one.
B.two.
C.three.
D.four.
Answer» A. one.

18.The time horizon in operational environment is ___________.
A.30-60 days.
B.60-90 days.
C.90-120 days.
D.120-150 days.
Answer» B. 60-90 days.

19.The key used in operational environment may not have an element of__________.
A.time.
B.cost.
C.frequency.
D.quality.
Answer» A. time.

20.Data can be updated in _____environment.
A.data warehouse.
B.data mining.
C.operational.
D.informational.
Answer» C. operational.

21.Record cannot be updated in _____________.
A.oltp
B.files
C.rdbms
D.data warehouse
Answer» D. data warehouse

22.The source of all data warehouse data is the____________.
A.operational environment.
B.informal environment.
C.formal environment.
D.technology environment.
Answer» A. operational environment.

23.Data warehouse contains_____________data that is never found in the operational environment.
A.normalized.
B.informational.
C.summary.
D.denormalized.
Answer» C. summary.

24.The modern CASE tools belong to _______ category.
A.analysis.
B.development
C.coding
D.delivery
Answer» A. analysis.

26.Detail data in single fact table is otherwise known as__________.
A.monoatomic data.
B.diatomic data.
C.atomic data.
D.multiatomic data.
Answer» C. atomic data.

27._______test is used in an online transactional processing environment.
A.mega.
B.micro.
C.macro.
D.acid.
Answer» D. acid.

28.___________ is a good alternative to the star schema.
A.star schema.
B.snowflake schema.
C.fact constellation.
D.star-snowflake schema.
Answer» C. fact constellation.

29.The biggest drawback of the level indicator in the classic star-schema is that it limits_________.
A.quantify.
B.qualify.
C.flexibility.
D.ability.
Answer» C. flexibility.

30.A data warehouse is _____________.
A.updated by end users.
B.contains numerous naming conventions and formats
C.organized around important subject areas.
D.contains only current data.
Answer» C. organized around important subject areas.

31.An operational system is _____________.
A.used to run the business in real time and is based on historical data.
B.used to run the business in real time and is based on current data.
C.used to support decision making and is based on current data.
D.used to support decision making and is based on historical data.
Answer» B. used to run the business in real time and is based on current data.

32.The generic two-level data warehouse architecture includes __________.
A.at least one data mart.
B.data that can extracted from numerous internal and external sources.
C.near real-time updates.
D.far real-time updates.
Answer» B. data that can extracted from numerous internal and external sources.

 (1)

33.The active data warehouse architecture includes __________
A.at least one data mart.
B.data that can extracted from numerous internal and external sources.
C.near real-time updates.
D.all of the above.
Answer» D. all of the above.

34.Reconciled data is ___________.
A.data stored in the various operational systems throughout the organization.
B.current data intended to be the single source for all decision support systems.
C.data stored in one operational system in the organization.
D.data that has been selected and formatted for end-user support applications.
Answer» B. current data intended to be the single source for all decision support systems.

35.The extract process is ______.
A.capturing all of the data contained in various operational systems.
B.capturing a subset of the data contained in various operational systems.
C.capturing all of the data contained in various decision support systems.
D.capturing a subset of the data contained in various decision support systems.
Answer» B. capturing a subset of the data contained in various operational systems.

36.Data scrubbing is _____________.
A.a process to reject data from the data warehouse and to create the necessary indexes.
B.a process to load the data in the data warehouse and to create the necessary indexes.
C.a process to upgrade the quality of data after it is moved into a data warehouse.
D.a process to upgrade the quality of data before it is moved into a data warehouse
Answer» D. a process to upgrade the quality of data before it is moved into a data warehouse

37.The load and index is ______________.
A.a process to reject data from the data warehouse and to create the necessary indexes.
B.a process to load the data in the data warehouse and to create the necessary indexes.
C.a process to upgrade the quality of data after it is moved into a data warehouse.
D.a process to upgrade the quality of data before it is moved into a data warehouse.
Answer» B. a process to load the data in the data warehouse and to create the necessary indexes.

38.Data transformation includes __________.
A.a process to change data from a detailed level to a summary level.
B.a process to change data from a summary level to a detailed level.
C.joining data from one source into various sources of data.
D.separating data from one source into various sources of data.
Answer» A. a process to change data from a detailed level to a summary level.

39.____________ is called a multifield transformation.
A.converting data from one field into multiple fields.
B.converting data from fields into field.
C.converting data from double fields into multiple fields.
D.converting data from one field to one field.
Answer» A. converting data from one field into multiple fields.

40.The type of relationship in star schema is __________________.
A.many-to-many.
B.one-to-one.
C.one-to-many.
D.many-to-one.
Answer» C. one-to-many.

41.Fact tables are ___________.
A.completely demoralized.
B.partially demoralized.
C.completely normalized.
D.partially normalized.
Answer» C. completely normalized.

42._______________ is the goal of data mining.
A.to explain some observed event or condition.
B.to confirm that data exists.
C.to analyze data for expected relationships.
D.to create a new data warehouse.
Answer» A. to explain some observed event or condition.

43.Business Intelligence and data warehousing is used for ________.
A.forecasting.
B.data mining.
C.analysis of large volumes of product sales data.
D.all of the above.
Answer» D. all of the above.

44.The data administration subsystem helps you perform all of the following, except__________.
A.backups and recovery.
B.query optimization.
C.security management.
D.create, change, and delete information.
Answer» D. create, change, and delete information.

45.The most common source of change data in refreshing a data warehouse is _______.
A.queryable change data.
B.cooperative change data.
C.logged change data.
D.snapshot change data.
Answer» A. queryable change data.

46.________ are responsible for running queries and reports against data warehouse tables.
A.hardware.
B.software.
C.end users.
D.middle ware.
Answer» C. end users.

47.Query tool is meant for __________.
A.data acquisition.
B.information delivery.
C.information exchange.
D.communication.
Answer» A. data acquisition.

48.Classification rules are extracted from _____________.
A.root node.
B.decision tree.
C.siblings.
D.branches.
Answer» B. decision tree.

49.Dimensionality reduction reduces the data set size by removing ____________.
A.relevant attributes.
B.irrelevant attributes.
C.derived attributes.
D.composite attributes.
Answer» B. irrelevant attributes.

51.Effect of one attribute value on a given class is independent of values of other attribute is called _________.
A.value independence.
B.class conditional independence.
C.conditional independence.
D.unconditional independence.
Answer» A. value independence.

52.The main organizational justification for implementing a data warehouse is to provide ______.
A.cheaper ways of handling transportation.
B.decision support.
C.storing large volume of data.
D.access to data.
Answer» C. storing large volume of data.

53.Multidimensional database is otherwise known as____________.
A.rdbms
B.dbms
C.extended rdbms
D.extended dbms
Answer» B. dbms

54.Data warehouse architecture is based on ______________.
A.dbms.
B.rdbms.
C.sybase.
D.sql server.
Answer» B. rdbms.

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55.Source data from the warehouse comes from _______________.
A.ods.
B.tds.
C.mddb.
D.ordbms.
Answer» A. ods.

56.________________ is a data transformation process.
A.comparison.
B.projection.
C.selection.
D.filtering.
Answer» D. filtering.

57.The technology area associated with CRM is _______________.
A.specialization.
B.generalization.
C.personalization.
D.summarization.
Answer» C. personalization.

58.SMP stands for _______________.
A.symmetric multiprocessor.
B.symmetric multiprogramming.
C.symmetric metaprogramming.
D.symmetric microprogramming.
Answer» A. symmetric multiprocessor.
view more info and meaning of SMP

59.__________ are designed to overcome any limitations placed on the warehouse by the nature of the relational data model.
A.operational database.
B.relational database.
C.multidimensional database.
D.data repository.
Answer» C. multidimensional database.

60.MDDB stands for ___________.
A.multiple data doubling.
B.multidimensional databases.
C.multiple double dimension.
D.multi-dimension doubling.
Answer» B. multidimensional databases.

61.______________ is data about data.
A.metadata.
B.microdata.
C.minidata.
D.multidata.
Answer» A. metadata.

62.___________ is an important functional component of the metadata.
A.digital directory.
B.repository.
C.information directory.
D.data dictionary.
Answer» C. information directory.

63.EIS stands for ______________.
A.extended interface system.
B.executive interface system.
C.executive information system.
D.extendable information system.
Answer» C. executive information system.
view more info and meaning of EIS

64.___________ is data collected from natural systems.
A.mri scan.
B.ods data.
C.statistical data.
D.historical data.
Answer» A. mri scan.

65._______________ is an example of application development environments.
A.visual basic.
B.oracle.
C.sybase.
D.sql server.
Answer» A. visual basic.

66.The term that is not associated with data cleaning process is ______.
A.domain consistency.
B.deduplication.
C.disambiguation.
D.segmentation.
Answer» D. segmentation.

67.____________ are some popular OLAP tools.
A.metacube, informix.
B.oracle express, essbase.
C.holap.
D.molap.
Answer» A. metacube, informix.

68.Capability of data mining is to build ___________ models.
A.retrospective.
B.interrogative.
C.predictive.
D.imperative.
Answer» C. predictive.

69._____________ is a process of determining the preference of customer’s majority.
A.association.
B.preferencing.
C.segmentation.
D.classification.
Answer» B. preferencing.

70.Strategic value of data mining is ______________.
A.cost-sensitive.
B.work-sensitive.
C.time-sensitive.
D.technical-sensitive.
Answer» C. time-sensitive.

71.____________ proposed the approach for data integration issues.
A.ralph campbell.
B.ralph kimball.
C.john raphlin.
D.james gosling.
Answer» B. ralph kimball.

72.The terms equality and roll up are associated with ____________.
A.olap.
B.visualization.
C.data mart.
D.decision tree.
Answer» C. data mart.

73.Exceptional reporting in data warehousing is otherwise called as __________.
A.exception.
B.alerts.
C.errors.
D.bugs.
Answer» B. alerts.

74.____________ is a metadata repository.
A.prism solution directory manager.
B.corba.
C.stunt.
D.cobweb.
Answer» A. prism solution directory manager.

76.The full form of KDD is _________.
A.knowledge database.
B.knowledge discovery in database.
C.knowledge data house.
D.knowledge data definition.
Answer» B. knowledge discovery in database.

77.The first International conference on KDD was held in the year _____________.
A.1996.
B.1997.
C.1995.
D.1994.
Answer» C. 1995.

78.Removing duplicate records is a process called _____________.
A.recovery.
B.data cleaning.
C.data cleansing.
D.data pruning.
Answer» B. data cleaning.

79.____________ contains information that gives users an easy-to-understand perspective of the information stored in the data warehouse.
A.business metadata.
B.technical metadata.
C.operational metadata.
D.financial metadata.
Answer» A. business metadata.

80._______________ helps to integrate, maintain and view the contents of the data warehousing system.
A.business directory.
B.information directory.
C.data dictionary.
D.database.
Answer» B. information directory.

81.Discovery of cross-sales opportunities is called ________________.
A.segmentation.
B.visualization.
C.correction.
D.association.
Answer» D. association.

82.Data marts that incorporate data mining tools to extract sets of data are called ______.
A.independent data mart.
B.dependent data marts.
C.intra-entry data mart.
D.inter-entry data mart.
Answer» B. dependent data marts.

83.____________ can generate programs itself, enabling it to carry out new tasks.
A.automated system.
B.decision making system.
C.self-learning system.
D.productivity system.
Answer» D. productivity system.

84.The power of self-learning system lies in __________.
A.cost.
B.speed.
C.accuracy.
D.simplicity.
Answer» C. accuracy.

85.Building the informational database is done with the help of _______.
A.transformation or propagation tools.
B.transformation tools only.
C.propagation tools only.
D.extraction tools.
Answer» A. transformation or propagation tools.

86.How many components are there in a data warehouse?
A.two.
B.three.
C.four.
D.five.
Answer» D. five.

87.Which of the following is not a component of a data warehouse?
A.metadata.
B.current detail data.
C.lightly summarized data.
D.component key.
Answer» D. component key.

88.________ is data that is distilled from the low level of detail found at the current detailed leve.
A.highly summarized data.
B.lightly summarized data.
C.metadata.
D.older detail data.
Answer» B. lightly summarized data.

89.Highly summarized data is _______.
A.compact and easily accessible.
B.compact and expensive.
C.compact and hardly accessible.
D.compact.
Answer» A. compact and easily accessible.

90.A directory to help the DSS analyst locate the contents of the data warehouse is seen in ______.
A.current detail data.
B.lightly summarized data.
C.metadata.
D.older detail data.
Answer» C. metadata.

91.Metadata contains atleast _________.
A.the structure of the data.
B.the algorithms used for summarization.
C.the mapping from the operational environment to the data warehouse.
D.all of the above.
Answer» D. all of the above.

92.Which of the following is not a old detail storage medium?
A.phot optical storage.
B.raid.
C.microfinche.
D.pen drive.
Answer» D. pen drive.

93.The data from the operational environment enter _______ of data warehouse.
A.current detail data.
B.older detail data.
C.lightly summarized data.
D.highly summarized data.
Answer» A. current detail data.

94.The data in current detail level resides till ________ event occurs.
A.purge.
B.summarization.
C.archieved.
D.all of the above.
Answer» D. all of the above.

95.The dimension tables describe the _________.
A.entities.
B.facts.
C.keys.
D.units of measures.
Answer» B. facts.

96.The granularity of the fact is the _____ of detail at which it is recorded.
A.transformation.
B.summarization.
C.level.
D.transformation and summarization.
Answer» C. level.

97.Which of the following is not a primary grain in analytical modeling?
A.transaction.
B.periodic snapshot.
C.accumulating snapshot.
D.all of the above.
Answer» B. periodic snapshot.

98.Granularity is determined by ______.
A.number of parts to a key.
B.granularity of those parts.
C.both a and b.
D.none of the above.
Answer» C. both a and b.

99.___________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity.
A.additivity.
B.granularity.
C.functional dependency.
D.dimensionality.
Answer» C. functional dependency.

101.A fact is said to be partially additive if ___________.
A.it is additive over every dimension of its dimensionality.
B.additive over atleast one but not all of the dimensions.
C.not additive over any dimension.
D.none of the above.
Answer» B. additive over atleast one but not all of the dimensions.

102.A fact is said to be non-additive if ___________.
A.it is additive over every dimension of its dimensionality.
B.additive over atleast one but not all of the dimensions.
C.not additive over any dimension.
D.none of the above.
Answer» C. not additive over any dimension.

103.Non-additive measures can often combined with additive measures to create new _________.
A.additive measures.
B.non-additive measures.
C.partially additive.
D.all of the above.
Answer» A. additive measures.

104.A fact representing cumulative sales units over a day at a store for a product is a _________.
A.additive fact.
B.fully additive fact.
C.partially additive fact.
D.non-additive fact.
Answer» B. fully additive fact.

105.____________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity.
A.additivity.
B.granularity.
C.functional dependency.
D.dependency.
Answer» C. functional dependency.

106.Which of the following is the other name of Data mining?
A.exploratory data analysis.
B.data driven discovery.
C.deductive learning.
D.all of the above.
Answer» D. all of the above.

107.Which of the following is a predictive model?
A.clustering.
B.regression.
C.summarization.
D.association rules.
Answer» B. regression.

108.Which of the following is a descriptive model?
A.classification.
B.regression.
C.sequence discovery.
D.association rules.
Answer» C. sequence discovery.

109.A ___________ model identifies patterns or relationships.
A.descriptive.
B.predictive.
C.regression.
D.time series analysis.
Answer» A. descriptive.

110.A predictive model makes use of ________.
A.current data.
B.historical data.
C.both current and historical data.
D.assumptions.
Answer» B. historical data.

111.____________ maps data into predefined groups.
A.regression.
B.time series analysis
C.prediction.
D.classification.
Answer» D. classification.

112.__________ is used to map a data item to a real valued prediction variable.
A.regression.
B.time series analysis.
C.prediction.
D.classification.
Answer» B. time series analysis.

113.In ____________, the value of an attribute is examined as it varies over time.
A.regression.
B.time series analysis.
C.sequence discovery.
D.prediction.
Answer» B. time series analysis.

114.In ________ the groups are not predefined.
A.association rules.
B.summarization.
C.clustering.
D.prediction.
Answer» C. clustering.

115.Link Analysis is otherwise called as ___________.
A.affinity analysis.
B.association rules.
C.both a & b.
D.prediction.
Answer» C. both a & b.

116._________ is a the input to KDD.
A.data.
B.information.
C.query.
D.process.
Answer» A. data.

117.The output of KDD is __________.
A.data.
B.information.
C.query.
D.useful information.
Answer» D. useful information.

118.The KDD process consists of ________ steps.
A.three.
B.four.
C.five.
D.six.
Answer» C. five.

119.Treating incorrect or missing data is called as ___________.
A.selection.
B.preprocessing.
C.transformation.
D.interpretation.
Answer» B. preprocessing.

120.Converting data from different sources into a common format for processing is called as ________.
A.selection.
B.preprocessing.
C.transformation.
D.interpretation.
Answer» C. transformation.

121.Various visualization techniques are used in ___________ step of KDD.
A.selection.
B.transformaion.
C.data mining.
D.interpretation.
Answer» D. interpretation.

122.Extreme values that occur infrequently are called as _________.
A.outliers.
B.rare values.
C.dimensionality reduction.
D.all of the above.
Answer» A. outliers.

123.Box plot and scatter diagram techniques are _______.
A.graphical.
B.geometric.
C.icon-based.
D.pixel-based.
Answer» B. geometric.

124.__________ is used to proceed from very specific knowledge to more general information.
A.induction.
B.compression.
C.approximation.
D.substitution.
Answer» A. induction.

126._____________ helps to uncover hidden information about the data.
A.induction.
B.compression.
C.approximation.
D.summarization.
Answer» C. approximation.

127._______ are needed to identify training data and desired results.
A.programmers.
B.designers.
C.users.
D.administrators.
Answer» C. users.

128.Overfitting occurs when a model _________.
A.does fit in future states.
B.does not fit in future states.
C.does fit in current state.
D.does not fit in current state.
Answer» B. does not fit in future states.

129.The problem of dimensionality curse involves ___________.
A.the use of some attributes may interfere with the correct completion of a data mining task.
B.the use of some attributes may simply increase the overall complexity.
C.some may decrease the efficiency of the algorithm.
D.all of the above.
Answer» D. all of the above.

130.Incorrect or invalid data is known as _________.
A.changing data.
B.noisy data.
C.outliers.
D.missing data.
Answer» B. noisy data.

131.ROI is an acronym of ________.
A.return on investment.
B.return on information.
C.repetition of information.
D.runtime of instruction
Answer» A. return on investment.

132.The ____________ of data could result in the disclosure of information that is deemed to be confidential.
A.authorized use.
B.unauthorized use.
C.authenticated use.
D.unauthenticated use.
Answer» B. unauthorized use.

133.___________ data are noisy and have many missing attribute values.
A.preprocessed.
B.cleaned.
C.real-world.
D.transformed.
Answer» C. real-world.

134.The rise of DBMS occurred in early ___________.
A.1950\s.
B.1960\s
C.1970\s
D.1980\s.
Answer» C. 1970\s

135.SQL stand for _________.
A.standard query language.
B.structured query language.
C.standard quick list.
D.structured query list.
Answer» B. structured query language.

136.Which of the following is not a data mining metric?
A.space complexity.
B.time complexity.
C.roi.
D.all of the above.
Answer» D. all of the above.

137.Reducing the number of attributes to solve the high dimensionality problem is called as ________.
A.dimensionality curse.
B.dimensionality reduction.
C.cleaning.
D.overfitting.
Answer» B. dimensionality reduction.

138.Data that are not of interest to the data mining task is called as ______.
A.missing data.
B.changing data.
C.irrelevant data.
D.noisy data.
Answer» C. irrelevant data.

 (1)

139.______ are effective tools to attack the scalability problem.
A.sampling.
B.parallelization
C.both a & b.
D.none of the above.
Answer» C. both a & b.

140.Market-basket problem was formulated by __________.
A.agrawal et al.
B.steve et al.
C.toda et al.
D.simon et al.
Answer» A. agrawal et al.

141.Data mining helps in __________.
A.inventory management.
B.sales promotion strategies.
C.marketing strategies.
D.all of the above.
Answer» D. all of the above.

142.The proportion of transaction supporting X in T is called _________.
A.confidence.
B.support.
C.support count.
D.all of the above.
Answer» B. support.

143.The absolute number of transactions supporting X in T is called ___________.
A.confidence.
B.support.
C.support count.
D.none of the above.
Answer» C. support count.

144.The value that says that transactions in D that support X also support Y is called ______________.
A.confidence.
B.support.
C.support count.
D.none of the above.
Answer» A. confidence.

145.If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the support of bread and jam is _______.
A.2%
B.20%
C.3%
D.30%
Answer» A. 2%

146.7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the confidence of buying bread with jam is _______.
A.33.33%
B.66.66%
C.45%
D.50%
Answer» D. 50%

147.The left hand side of an association rule is called __________.
A.consequent.
B.onset.
C.antecedent.
D.precedent.
Answer» C. antecedent.

148.The right hand side of an association rule is called _____.
A.consequent.
B.onset.
C.antecedent.
D.precedent.
Answer» A. consequent.

149.Which of the following is not a desirable feature of any efficient algorithm?
A.to reduce number of input operations.
B.to reduce number of output operations.
C.to be efficient in computing.
D.to have maximal code length.
Answer» D. to have maximal code length.

151.If a set is a frequent set and no superset of this set is a frequent set, then it is called ________.
A.maximal frequent set.
B.border set.
C.lattice.
D.infrequent sets.
Answer» A. maximal frequent set.

152.Any subset of a frequent set is a frequent set. This is ___________.
A.upward closure property.
B.downward closure property.
C.maximal frequent set.
D.border set.
Answer» B. downward closure property.

153.Any superset of an infrequent set is an infrequent set. This is _______.
A.maximal frequent set.
B.border set.
C.upward closure property.
D.downward closure property.
Answer» C. upward closure property.

154.If an itemset is not a frequent set and no superset of this is a frequent set, then it is _______.
A.maximal frequent set
B.border set.
C.upward closure property.
D.downward closure property.
Answer» B. border set.

155.A priori algorithm is otherwise called as __________.
A.width-wise algorithm.
B.level-wise algorithm.
C.pincer-search algorithm.
D.fp growth algorithm.
Answer» B. level-wise algorithm.

156.The A Priori algorithm is a ___________.
A.top-down search.
B.breadth first search.
C.depth first search.
D.bottom-up search.
Answer» D. bottom-up search.

157.The first phase of A Priori algorithm is _______.
A.candidate generation.
B.itemset generation.
C.pruning.
D.partitioning.
Answer» A. candidate generation.

158.The second phaase of A Priori algorithm is ____________.
A.candidate generation.
B.itemset generation.
C.pruning.
D.partitioning.
Answer» C. pruning.

159.The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent, from being considered for counting support.
A.candidate generation.
B.pruning.
C.partitioning.
D.itemset eliminations.
Answer» B. pruning.

160.The a priori frequent itemset discovery algorithm moves _______ in the lattice.
A.upward.
B.downward.
C.breadthwise.
D.both upward and downward.
Answer» A. upward.

161.After the pruning of a priori algorithm, _______ will remain.
A.only candidate set.
B.no candidate set.
C.only border set.
D.no border set.
Answer» B. no candidate set.

162.The number of iterations in a priori ___________.
A.increases with the size of the maximum frequent set.
B.decreases with increase in size of the maximum frequent set.
C.increases with the size of the data.
D.decreases with the increase in size of the data.
Answer» A. increases with the size of the maximum frequent set.

163.MFCS is the acronym of _____.
A.maximum frequency control set.
B.minimal frequency control set.
C.maximal frequent candidate set.
D.minimal frequent candidate set.
Answer» C. maximal frequent candidate set.

164.Dynamuc Itemset Counting Algorithm was proposed by ____.
A.bin et al.
B.argawal et at.
C.toda et al.
D.simon et at.
Answer» A. bin et al.

165.Itemsets in the ______ category of structures have a counter and the stop number with them.
A.dashed.
B.circle.
C.box.
D.solid.
Answer» A. dashed.

166.The itemsets in the _______category structures are not subjected to any counting.
A.dashes.
B.box.
C.solid.
D.circle.
Answer» C. solid.

167.Certain itemsets in the dashed circle whose support count reach support value during an iteration move into the ______.
A.dashed box.
B.solid circle.
C.solid box.
D.none of the above.
Answer» A. dashed box.

168.Certain itemsets enter afresh into the system and get into the _______, which are essentially the supersets of the itemsets that move from the dashed circle to the dashed box.
A.dashed box.
B.solid circle.
C.solid box.
D.dashed circle.
Answer» D. dashed circle.

169.The itemsets that have completed on full pass move from dashed circle to ________.
A.dashed box.
B.solid circle.
C.solid box.
D.none of the above.
Answer» B. solid circle.

170.The FP-growth algorithm has ________ phases.
A.one.
B.two.
C.three.
D.four.
Answer» B. two.

171.A frequent pattern tree is a tree structure consisting of ________.
A.an item-prefix-tree.
B.a frequent-item-header table.
C.a frequent-item-node.
D.both a & b.
Answer» D. both a & b.

172.The non-root node of item-prefix-tree consists of ________ fields.
A.two.
B.three.
C.four.
D.five.
Answer» B. three.

173.The frequent-item-header-table consists of __________ fields.
A.only one.
B.two.
C.three.
D.four.
Answer» B. two.

174.The paths from root node to the nodes labelled ‘a’ are called __________.
A.transformed prefix path.
B.suffix subpath.
C.transformed suffix path.
D.prefix subpath.
Answer» D. prefix subpath.

176.The goal of _____ is to discover both the dense and sparse regions of a data set.
A.association rule.
B.classification.
C.clustering.
D.genetic algorithm.
Answer» C. clustering.

177.Which of the following is a clustering algorithm?
A.a priori.
B.clara.
C.pincer-search.
D.fp-growth.
Answer» B. clara.

178._______ clustering technique start with as many clusters as there are records, with each cluster having only one record.
A.agglomerative.
B.divisive.
C.partition.
D.numeric.
Answer» A. agglomerative.

179.__________ clustering techniques starts with all records in one cluster and then try to split that cluster into small pieces.
A.agglomerative.
B.divisive.
C.partition.
D.numeric.
Answer» B. divisive.

180.Which of the following is a data set in the popular UCI machine-learning repository?
A.clara.
B.cactus.
C.stirr.
D.mushroom.
Answer» D. mushroom.

181.In ________ algorithm each cluster is represented by the center of gravity of the cluster.
A.k-medoid.
B.k-means.
C.stirr.
D.rock.
Answer» B. k-means.

182.In ___________ each cluster is represented by one of the objects of the cluster located near the center.
A.k-medoid.
B.k-means.
C.stirr.
D.rock.
Answer» A. k-medoid.

183.Pick out a k-medoid algoithm.
A.dbscan.
B.birch.
C.pam.
D.cure.
Answer» C. pam.

184.Pick out a hierarchical clustering algorithm.
A.dbscan
B.birch.
C.pam.
D.cure.
Answer» B. birch.

185.CLARANS stands for _______.
A.clara net server.
B.clustering large application range network search.
C.clustering large applications based on randomized search.
D.clustering application randomized search.
Answer» C. clustering large applications based on randomized search.

186.BIRCH is a ________.
A.agglomerative clustering algorithm.
B.hierarchical algorithm.
C.hierarchical-agglomerative algorithm.
D.divisive.
Answer» C. hierarchical-agglomerative algorithm.

 (1)

187.The cluster features of different subclusters are maintained in a tree called ___________.
A.cf tree.
B.fp tree.
C.fp growth tree.
D.b tree.
Answer» A. cf tree.

188.The ________ algorithm is based on the observation that the frequent sets are normally very few in number compared to the set of all itemsets.
A.a priori.
B.clustering.
C.association rule.
D.partition.
Answer» D. partition.

189.The partition algorithm uses _______ scans of the databases to discover all frequent sets.
A.two.
B.four.
C.six.
D.eight.
Answer» A. two.

190.The basic idea of the apriori algorithm is to generate________ item sets of a particular size & scans the database.
A.candidate.
B.primary.
C.secondary.
D.superkey.
Answer» A. candidate.

191.An algorithm called________is used to generate the candidate item sets for each pass after the first.
A.apriori.
B.apriori-gen.
C.sampling.
D.partition.
Answer» B. apriori-gen.

192.The basic partition algorithm reduces the number of database scans to ________ & divides it into partitions.
A.one.
B.two.
C.three.
D.four.
Answer» B. two.

193.___________and prediction may be viewed as types of classification.
A.decision.
B.verification.
C.estimation.
D.illustration.
Answer» C. estimation.

194.___________can be thought of as classifying an attribute value into one of a set of possible classes.
A.estimation.
B.prediction.
C.identification.
D.clarification.
Answer» B. prediction.

195.Prediction can be viewed as forecasting a_________value.
A.non-continuous.
B.constant.
C.continuous.
D.variable.
Answer» C. continuous.

196._________data consists of sample input data as well as the classification assignment for the data.
A.missing.
B.measuring.
C.non-training.
D.training.
Answer» D. training.

197.Rule based classification algorithms generate ______ rule to perform the classification.
A.if-then.
B.while.
C.do while.
D.switch.
Answer» A. if-then.

198.____________ are a different paradigm for computing which draws its inspiration from neuroscience.
A.computer networks.
B.neural networks.
C.mobile networks.
D.artificial networks.
Answer» B. neural networks.

199.The human brain consists of a network of ___________.
A.neurons.
B.cells.
C.tissue.
D.muscles.
Answer» A. neurons.

201.The ___________is a long, single fibre that originates from the cell body.
A.axon.
B.neuron.
C.dendrites.
D.strands.
Answer» A. axon.

202.A single axon makes ___________ of synapses with other neurons.
A.ones.
B.hundreds.
C.thousands.
D.millions.
Answer» C. thousands.

203._____________ is a complex chemical process in neural networks.
A.receiving process.
B.sending process.
C.transmission process.
D.switching process.
Answer» C. transmission process.

204._________ is the connectivity of the neuron that give simple devices their real power. a. b. c. d.
A.water.
B.air.
C.power.
D.fire.
Answer» D. fire.

205.__________ are highly simplified models of biological neurons.
A.artificial neurons.
B.computational neurons.
C.biological neurons.
D.technological neurons.
Answer» A. artificial neurons.

206.The biological neuron’s _________ is a continuous function rather than a step function.
A.read.
B.write.
C.output.
D.input.
Answer» C. output.

207.The threshold function is replaced by continuous functions called ________ functions.
A.activation.
B.deactivation.
C.dynamic.
D.standard.
Answer» A. activation.

208.The sigmoid function also knows as __________functions.
A.regression.
B.logistic.
C.probability.
D.neural.
Answer» B. logistic.

209.MLP stands for ______________________.
A.mono layer perception.
B.many layer perception.
C.more layer perception.
D.multi layer perception.
Answer» D. multi layer perception.

210.In a feed- forward networks, the conncetions between layers are ___________ from input to output.
A.bidirectional.
B.unidirectional.
C.multidirectional.
D.directional.
Answer» B. unidirectional.

211.The network topology is constrained to be __________________.
A.feedforward.
B.feedbackward.
C.feed free.
D.feed busy.
Answer» A. feedforward.

212.RBF stands for _____________.
A.radial basis function.
B.radial bio function.
C.radial big function.
D.radial bi function.
Answer» A. radial basis function.
view more info and meaning of RBF

213.RBF have only _______________ hidden layer.
A.four.
B.three.
C.two.
D.one.
Answer» D. one.

214.RBF hidden layer units have a receptive field which has a ____________; that is, a particular input value at which they have a maximal output.
A.top.
B.bottom.
C.centre.
D.border.
Answer» C. centre.

215.___________ training may be used when a clear link between input data sets and target output values does not exist.
A.competitive.
B.perception.
C.supervised.
D.unsupervised.
Answer» D. unsupervised.

216.___________ employs the supervised mode of learning.
A.rbf.
B.mlp.
C.mlp & rbf.
D.ann.
Answer» C. mlp & rbf.

217.________________ design involves deciding on their centres and the sharpness of their Gaussians.
A.dr.
B.and.
C.xor.
D.rbf.
Answer» D. rbf.

218.___________ is the most widely applied neural network technique.
A.abc.
B.plm.
C.lmp.
D.mlp.
Answer» D. mlp.

219.SOM is an acronym of _______________.
A.self-organizing map.
B.self origin map.
C.single organizing map.
D.simple origin map.
Answer» A. self-organizing map.

220.____________ is one of the most popular models in the unsupervised framework.
A.som.
B.sam.
C.osm.
D.mso.
Answer» A. som.

221.The actual amount of reduction at each learning step may be guided by _________.
A.learning cost.
B.learning level.
C.learning rate.
D.learning time.
Answer» C. learning rate.

222.The SOM was a neural network model developed by ________.
A.simon king.
B.teuvokohonen.
C.tomoki toda.
D.julia.
Answer» B. teuvokohonen.

223.SOM was developed during ____________.
A.1970-80.
B.1980-90.
C.1990 -60.
D.1979 -82.
Answer» D. 1979 -82.

224.Investment analysis used in neural networks is to predict the movement of _________ from previous data.
A.engines.
B.stock.
C.patterns.
D.models.
Answer» B. stock.

226.GA stands for _______________.
A.genetic algorithm
B.gene algorithm.
C.general algorithm.
D.geo algorithm.
Answer» A. genetic algorithm
view more info and meaning of GA

227.GA was introduced in the year __________.
A.1955.
B.1965.
C.1975.
D.1985.
Answer» C. 1975.

228.Genetic algorithms are search algorithms based on the mechanics of natural_______.
A.systems.
B.genetics.
C.logistics.
D.statistics.
Answer» B. genetics.

229.GAs were developed in the early _____________.
A.1970.
B.1960.
C.1950.
D.1940.
Answer» A. 1970.

230.The RSES system was developed in ___________.
A.poland.
B.italy.
C.england.
D.america.
Answer» A. poland.

231.Crossover is used to _______.
A.recombine the population\s genetic material.
B.introduce new genetic structures in the population.
C.to modify the population\s genetic material.
D.all of the above.
Answer» A. recombine the population\s genetic material.

232.The mutation operator ______.
A.recombine the population\s genetic material.
B.introduce new genetic structures in the population.
C.to modify the population\s genetic material.
D.all of the above.
Answer» B. introduce new genetic structures in the population.

233.Which of the following is an operation in genetic algorithm?
A.inversion.
B.dominance.
C.genetic edge recombination.
D.all of the above.
Answer» D. all of the above.

234.. ___________ is a system created for rule induction.
A.rbs.
B.cbs.
C.dbs.
D.lers.
Answer» D. lers.

235.NLP stands for _________.
A.non language process.
B.nature level program.
C.natural language page.
D.natural language processing.
Answer» D. natural language processing.
view more info and meaning of NLP

236.Web content mining describes the discovery of useful information from the _______contents.
A.text.
B.web.
C.page.
D.level.
Answer» B. web.

237.Research on mining multi-types of data is termed as _______ data.
A.graphics.
B.multimedia.
C.meta.
D.digital.
Answer» B. multimedia.

238._______ mining is concerned with discovering the model underlying the link structures of the web.
A.data structure.
B.web structure.
C.text structure.
D.image structure.
Answer» B. web structure.

239._________ is the way of studying the web link structure.
A.computer network.
B.physical network.
C.social network.
D.logical network.
Answer» C. social network.

240.The ________ propose a measure of standing a node based on path counting.
A.open web.
B.close web.
C.link web.
D.hidden web.
Answer» B. close web.

241.In web mining, _______ is used to find natural groupings of users, pages, etc.
A.clustering.
B.associations.
C.sequential analysis.
D.classification.
Answer» A. clustering.

242.In web mining, _________ is used to know the order in which URLs tend to be accessed.
A.clustering.
B.associations.
C.sequential analysis.
D.classification.
Answer» C. sequential analysis.

243.In web mining, _________ is used to know which URLs tend to be requested together.
A.clustering.
B.associations.
C.sequential analysis.
D.classification.
Answer» B. associations.

244.__________ describes the discovery of useful information from the web contents.
A.web content mining.
B.web structure mining.
C.web usage mining.
D.all of the above.
Answer» A. web content mining.

245._______ is concerned with discovering the model underlying the link structures of the web.
A.web content mining.
B.web structure mining.
C.web usage mining.
D.all of the above.
Answer» B. web structure mining.

246.The ___________ engine for a data warehouse supports query-triggered usage of data
A.nntp
B.smtp
C.olap
D.pop
Answer» C. olap

247.________ displays of data such as maps, charts and other graphical representation allow data to be presented compactly to the users.
A.hidden
B.visual
C.obscured
D.concealed
Answer» B. visual

248.__________ is a subject-oriented, integrated, time-variant, nonvolatile collection of data in support of management decisions.
A.data mining.
B.data warehousing.
C.web mining.
D.text mining.
Answer» B. data warehousing.

249.The important aspect of the data warehouse environment is that data found within the data warehouse is___________.
A.subject-oriented.
B.time-variant.
C.integrated.
D.all of the above.
Answer» D. all of the above.

251.Data redundancy between the environments results in less than ____________percent.
A.one.
B.two.
C.three.
D.four.
Answer» A. one.

252.Bill Inmon has estimated___________of the time required to build a data warehouse, is consumed in the conversion process.
A.10 percent.
B.20 percent.
C.40 percent
D.80 percent.
Answer» D. 80 percent.

253.The biggest drawback of the level indicator in the classic star-schema is that it limits_________
A.quantify.
B.qualify.
C.flexibility.
D.ability.
Answer» C. flexibility.

254.Maintenance of cache consistency is the limitation of __________________.
A.numa.
B.unam.
C.mpp.
D.pmp.
Answer» C. mpp.

255.___________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity.
A.additivity.
B.granularity.
C.functional dependency.
D.dimensionality.
Answer» C. functional dependency.

256.Non-additive measures can often combined with additive measures to create new _________..
A.additive measures.
B.non-additive measures.
C.partially additive.
D.all of the above.
Answer» A. additive measures.

257.____________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity.
A.additivity.
B.granularity.
C.functional dependency.
D.dependency.
Answer» C. functional dependency.

258._____________ helps to uncover hidden information about the data..
A.induction.
B.compression.
C.approximation.
D.summarization.
Answer» C. approximation.

259.If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain jam, 10000 transaction contain both bread and jam. Then the support of bread and jam is _______.
A.2%
B.20%
C.3%
D.30%
Answer» A. 2%

260.7 If T consist of 500000 transactions, 20000 transaction contain bread, 30000 transaction contain
jam, 10000 transaction contain both bread and jam. Then the confidence of buying bread with jam is
_______.
A.33.33%
B.66.66%
C.45%
D.50%
Answer» D. 50%

261.The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent, from being considered for counting support.
A.candidate generation.
B.pruning.
C.partitioning.
D.itemset eliminations.
Answer» B. pruning.

262.The transformed prefix paths of a node ‘a’ form a truncated database of pattern which co-occur with a is called _______.
A.suffix path.
B.fp-tree.
C.conditional pattern base.
D.prefix path.
Answer» C. conditional pattern base.

263.__________ clustering techniques starts with all records in one cluster and then try to split that cluster into small pieces.
A.agglomerative.
B.divisive.
C.partition.
D.numeric.
Answer» B. divisive.

264.BIRCH is a ________..
A.agglomerative clustering algorithm.
B.hierarchical algorithm.
C.hierarchical-agglomerative algorithm.
D.divisive.
Answer» C. hierarchical-agglomerative algorithm.

265.The ________ algorithm is based on the observation that the frequent sets are normally very few in number compared to the set of all itemsets.
A.a priori.
B.clustering.
C.association rule.
D.partition.
Answer» D. partition.

266.The basic idea of the apriori algorithm is to generate________ item sets of a particular size & scans the database.
A.candidate.
B.primary.
C.secondary.
D.superkey.
Answer» A. candidate.

267.________is the most well known association rule algorithm and is used in most commercial products.
A.apriori algorithm.
B.partition algorithm.
C.distributed algorithm.
D.pincer-search algorithm.
Answer» A. apriori algorithm.

268.An algorithm called________is used to generate the candidate item sets for each pass after the first.
A.apriori.
B.apriori-gen.
C.sampling.
D.partition.
Answer» B. apriori-gen.

269.___________can be thought of as classifying an attribute value into one of a set of possible classes.
A.estimation.
B.prediction.
C.identification.
D.clarification.
Answer» B. prediction.

270.____________ are a different paradigm for computing which draws its inspiration from neuroscience.
A.computer networks.
B.neural networks.
C.mobile networks.
D.artificial networks.
Answer» B. neural networks.

271.In a feed- forward networks, the conncetions between layers are ___________ from input to output.
A.bidirectional.
B.unidirectional.
C.multidirectional.
D.directional.
Answer» B. unidirectional.

272.___________ training may be used when a clear link between input data sets and target output values does not exist.
A.competitive.
B.perception.
C.supervised.
D.unsupervised.
Answer» D. unsupervised.

273.Investment analysis used in neural networks is to predict the movement of _________ from previous data.
A.engines.
B.stock.
C.patterns.
D.models.
Answer» B. stock.

274.SOMs are used to cluster a specific _____________ dataset containing information about the patient’s drugs etc.
A.physical.
B.logical.
C.medical.
D.technical.
Answer» C. medical.

276.A link is said to be _________ link if it is between pages with different domain names.
A.intrinsic.
B.transverse.
C.direct.
D.contrast.
Answer» B. transverse.

277.A link is said to be _______ link if it is between pages with the same domain name.
A.intrinsic.
B.transverse.
C.direct.
D.contrast.
Answer» A. intrinsic.

278.Patterns that can be discovered from a given database are which type
A.more than one type
B.multiple types always
C.one type only
D.no specific type
Answer» A. more than one type

279.A snowflake schema is which of the following types of tables?
A.fact
B.dimension
C.helper
D.all of the above
Answer» D. all of the above

280.Which one manages both current and historic transactions?
A.oltp
B.olap
C.spread sheet
D.xml
Answer» B. olap

281.Expansion for DSS in DW is__________.
A.decision support system
B.decision single system
C.data storable system
D.data support system
Answer» A. decision support system

282.__________describes the data contained in the data warehouse
A.relational data
B.operational data
C.meta data
D.informational data
Answer» C. meta data

283.Converting data from different sources into a common format for processing is called as________.
A.selection.
B.preprocessing
C.transformation
D.interpretation
Answer» C. transformation

284.Data warehousing is used in_______________
A.transaction system
B.database management system
C.decision support system
D.expert system
Answer» C. decision support system

285.Data warehouse is based on_____________
A.two dimensional model
B.three dimensional model
C.multi dimensional model
D.unidimensional model
Answer» C. multi dimensional model

286.Multidimensional model of data warehouse called as_____
A.data structure
B.table
C.tree
D.data cube
Answer» D. data cube

287.In data warehousing what is time-variant data?
A.data in the warehouse is only accurate and valid at some point in time or over time interval
B.data in the warehouse is always accurate and valid
C.data in the warehouse is not accurate
D.data in the warehouse is only accurate sometimes
Answer» A. data in the warehouse is only accurate and valid at some point in time or over time interval

288.What is a Star Schema?
A.a star schema consists of a fact table with a single table for each dimension
B.a star schema is a type of database system
C.a star schema is used when exporting data from the database
D.none of these
Answer» A. a star schema consists of a fact table with a single table for each dimension

289.What does the acronym ETL stands for?
A.explain,transfer and lead
B.extract,transform and load
C.extract,transfer and load
D.effect,transfer and load
Answer» B. extract,transform and load
view more info and meaning of ETL

290.Which small logical units do data warehouses hold large amounts of information?
A.data storage
B.data marts
C.access layers
D.data miners
Answer» B. data marts

291.Which one is correct for data warehousing?
A.it can be updated by end users
B.it can solve all business questions
C.it is designed for focus subject areas
D.it contains only current data
Answer» C. it is designed for focus subject areas

292.A fact table is related to dimensional table as a ___ relationship
A.1:m
B.m:n
C.m:1
D.1:1
Answer» C. m:1

293.Minkowski distance is a function used to find the distance between two
A.binary vectors
B.boolean-valued vectors
C.real-valued vectors
D.categorical vectors
Answer» C. real-valued vectors

294.Data set of designation {Professor, Assistant Professor, Associate Professor} is example of__________attribute.
A.continuous
B.ordinal
C.numeric
D.nominal
Answer» D. nominal

295.Identify the correct example of Nominal Attributes.
A.weight of person in kg
B.income categories – high, medium, low
C.mobile number
D.all above
Answer» B. income categories – high, medium, low

296.When objects are represented using single attribute, the proximity value 1 indicates :
A.objects are similar
B.objects are dissimilar
C.not equal
D.reflexive
Answer» A. objects are similar

297.Identity correct equation of Jacard Coefficient:
A.j= f11/f01+f10+f11
B.j= f11+f00/f01+f10+f11
C.j= f11+f00/f01+f10
D.none of these
Answer» A. j= f11/f01+f10+f11

298.What equation we get when r parameter =2 in Minskowski Distance formula?
A.manhattan distance
B.euclidean distance
C.lmaximum distance
D.all
Answer» B. euclidean distance

299.________is a generalization of Manhattan, Euclidean and Max Distance
A.euclidean distance
B.minkowski distance
C.manhattan distance
D.jaccard distance
Answer» B. minkowski distance

 (1)

301.Which is not the type of attribute used in distance measure?
A.ordinal
B.nominal
C.binay
D.rank
Answer» D. rank

302._____ method is used to find the distance between two objects represented by numerical attributes.
A.euclidean distance
B.minkowski distance
C.manhattan distance
D.all of these
Answer» D. all of these

303.Contingency table is prepared for _______ attribute data.
A.ordinal
B.nominal
C.binay
D.integer
Answer» C. binay

304.Which are the applications of proximity measures?
A.classification
B.clustering
C.knn classifier
D.all of these
Answer» D. all of these

305._________ matrix represents the distance between all objects in the dataset
A.confusion
B.dissimilarity
C.similarity
D.square
Answer» B. dissimilarity

306.If o1 and o2 are two objects and distance between these objects is zero then it means_____
A.o1 and o2 are totally similar
B.o1 and o2 are totally dissimilar
C.o1 and o2 are similar
D.o1 and o2 are partially dissimilar
Answer» A. o1 and o2 are totally similar

307.Identify the correct subtype of Binary attribute.
A.ordinal
B.asymmetric
C.symmetric
D.both b and c
Answer» D. both b and c

308._____ Lower when objects are more alike.
A.dissimilarity
B.recall
C.similarity
D.accuracy
Answer» A. dissimilarity

309.Adaptive system management is
A.It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations
B.Computational procedure that takes some value as input and produces some value as output.
C.Science of making machines performs tasks that would require intelligence when performed by humans
D.None of these
Answer» A. It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations

310.Algorithm is
A.It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations
B.Computational procedure that takes some value as input and produces some value as output
C.Science of making machines performs tasks that would require intelligence when performed by humans
D.None of these
Answer» B. Computational procedure that takes some value as input and produces some value as output

311.Background knowledge referred to
A.Additional acquaintance used by a learning algorithm to facilitate the learning process
B.A neural network that makes use of a hidden layer.
C.It is a form of automatic learning.
D.None of these
Answer» A. Additional acquaintance used by a learning algorithm to facilitate the learning process

312.Back propagation networks is
A.Additional acquaintance used by a learning algorithm to facilitate the learning process
B.A neural network that makes use of a hidden layer
C.It is a form of automatic learning.
D.None of these
Answer» B. A neural network that makes use of a hidden layer

313.Bayesian classifiers is
A.A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.
B.Any mechanism employed by a learning system to constrain the search space of a hypothesis.
C.An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
D.None of these
Answer» A. A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.

314.Bias is
A.A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.
B.Any mechanism employed by a learning system to constrain the search space of a hypothesis.
C.An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
D.None of these
Answer» B. Any mechanism employed by a learning system to constrain the search space of a hypothesis.

315.Case-based learning is
A.A class of learning algorithm that tries to find an optimum classification of a set of examples using the probabilistic theory.
B.Any mechanism employed by a learning system to constrain the search space of a hypothesis.
C.An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.
D.None of these
Answer» C. An approach to the design of learning algorithms that is inspired by the fact that when people encounter new situations, they often explain them by reference to familiar experiences, adapting the explanations to fit the new situation.

316.Binary attribute are
A.This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit
B.The natural environment of a certain species
C.Systems that can be used without knowledge of internal operations
D.None of these
Answer» A. This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit

317.Biotope are
A.This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit.
B.The natural environment of a certain species
C.Systems that can be used without knowledge of internal operations
D.None of these
Answer» B. The natural environment of a certain species

318.Black boxes
A.This takes only two values. In general, these values will be 0 and 1 and they can be coded as one bit.
B.The natural environment of a certain species
C.Systems that can be used without knowledge of internal operations
D.None of these
Answer» C. Systems that can be used without knowledge of internal operations

319.Artificial intelligence is
A.It uses machine-learning techniques. Here program can learn from past experience and adapt themselves to new situations
B.Computational procedure that takes some value as input and produces some value as output.
C.Science of making machines performs tasks that would require intelligence when performed by humans
D.None of these
Answer» C. Science of making machines performs tasks that would require intelligence when performed by humans

320.Cache is
A.It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic
B.The number of different values that a given attribute can take
C.A mathematical conception of space where the location of a point is given by reference to its distance from two or three axes intersecting at right angles
D.None of these
Answer» A. It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic

321.Cardinality of an attribute is
A.It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic
B.The number of different values that a given attribute can take
C.A mathematical conception of space where the location of a point is given by reference to its distance from two or three axes intersecting at right angles
D.None of these
Answer» B. The number of different values that a given attribute can take

322.Cartesian space is
A.It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic
B.The number of different values that a given attribute can take
C.A mathematical conception of space where the location of a point is given by reference to its distance from two or three axes intersecting at right angles
D.None of these
Answer» A. It is a memory buffer that is used to store data that is needed frequently by an algorithm in order to minimize input/ output traffic

323.Classification is
A.A subdivision of a set of examples into a number of classes
B.A measure of the accuracy, of the classification of a concept that is given by a certain theory
C.The task of assigning a classification to a set of examples
D.None of these
Answer» A. A subdivision of a set of examples into a number of classes

324.Classification accuracy is
A.A subdivision of a set of examples into a number of classes
B.Measure of the accuracy, of the classification of a concept that is given by a certain theory
C.The task of assigning a classification to a set of examples
D.None of these
Answer» B. Measure of the accuracy, of the classification of a concept that is given by a certain theory

326.Data is
A.Group of similar objects that differ significantly from other objects
B.Operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm
C.Symbolic representation of facts or ideas from which information can potentially be extract
Answer» C. Symbolic representation of facts or ideas from which information can potentially be extract

327.A definition of a concept is——if it recognizes all the instances of that concept.
A.Complete
B.Consistent
C.Constant
D.None of these
Answer» A. Complete

328.A definition or a concept is ———————if it does not classify any examples as coming within the concept
A.Complete
B.Consistent
C.Constant
D.None of these
Answer» B. Consistent

329.Classification task referred to
A.A subdivision of a set of examples into a number of classes
B.A measure of the accuracy, of the classification of a concept that is given by a certain theory
C.The task of assigning a classification to a set of examples
D.None of these
Answer» C. The task of assigning a classification to a set of examples

330.Database is
A.Large collection of data mostly stored in a computer system
B.The removal of noise errors and incorrect input from a database
C.The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships.
D.None of these
Answer» A. Large collection of data mostly stored in a computer system

331.Data cleaning is
A.Large collection of data mostly stored in a computer system
B.The removal of noise errors and incorrect input from a database
C.The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships.
D.None of these
Answer» B. The removal of noise errors and incorrect input from a database

332.Data dictionary is
A.Large collection of data mostly stored in a computer system
B.The removal of noise errors and incorrect input from a database
C.The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships.
D.None of these
Answer» C. The systematic description of the syntactic structure of a specific database. It describes the structure of the attributes the tables and foreign key relationships.

333.Data mining is
A.The actual discovery phase of a knowledge discovery process
B.The stage of selecting the right data for a KDD process
C.A subject-oriented integrated time- variant non-volatile collection of data in support of management
D.None of these
Answer» A. The actual discovery phase of a knowledge discovery process

334.Data selection is
A.The actual discovery phase of a knowledge discovery process
B.The stage of selecting the right data for a KDD process
C.A subject-oriented integrated time- variant non-volatile collection of data in support of management
D.None of these
Answer» B. The stage of selecting the right data for a KDD process

335.Data warehouse is
A.The actual discovery phase of a knowledge discovery process
B.The stage of selecting the right data for a KDD process
C.A subject-oriented integrated time- variant non-volatile collection of data in support of management
D.None of these
Answer» C. A subject-oriented integrated time- variant non-volatile collection of data in support of management

336.Coding is
A.Group of similar objects that differ significantly from other objects
B.Operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm
C.Symbolic representation of facts or ideas from which information can potentially be extracted
D.None of these
Answer» B. Operations on a database to transform or simplify data in order to prepare it for a machine-learning algorithm

337.DB/2 is
A.A family of relational database manage- ment systems marketed by IBM
B.Interactive systems that enable decision makers to use databases and models on a computer in order to solve ill- structured problems
C.It consists of nodes and branches starting from a single root node. Each node represents a test, or decision.
D.None of these
Answer» A. A family of relational database manage- ment systems marketed by IBM

338.Decision support systems (DSS) is
A.A family of relational database management systems marketed by IBM
B.Interactive systems that enable decision makers to use databases and models on a computer in order to solve ill- structured problems
C.It consists of nodes and branches starting from a single root node. Each node represents a test, or decision.
D.None of these
Answer» B. Interactive systems that enable decision makers to use databases and models on a computer in order to solve ill- structured problems

339.Decision trees is
A.A family of relational database management systems marketed by IBM
B.Interactive systems that enable decision makers to use databases and models on a computer in order to solve ill- structured problems
C.It consists of nodes and branches starting from a single root node. Each node represents a test, or decision.
D.None of these
Answer» C. It consists of nodes and branches starting from a single root node. Each node represents a test, or decision.

340.Deep knowledge referred to
A.It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information)
B.The process of executing implicit previously unknown and potentially useful information from dat(A)
C.An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.
D.None of these
Answer» A. It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information)

341.Discovery is
A.It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information).
B.The process of executing implicit previously unknown and potentially useful information from dat(A)
C.An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.
D.None of these
Answer» B. The process of executing implicit previously unknown and potentially useful information from dat(A)

342.DNA (Deoxyribonucleic acid)
A.It is hidden within a database and can only be recovered if one is given certain clues (an example IS encrypted information).
B.The process of executing implicit previously unknown and potentially useful information from dat (A)
C.An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.
D.None of these
Answer» C. An extremely complex molecule that occurs in human chromosomes and that carries genetic information in the form of genes.

343.Enrichment is
A.A stage of the KDD process in which new data is added to the existing selection
B.The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them.
C.The distance between two points as calculated using the Pythagoras theorem.
D.None of these
Answer» A. A stage of the KDD process in which new data is added to the existing selection

344.Enumeration is referred to
A.A stage of the KDD process in which new data is added to the existing selection.
B.The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them
C.The distance between two points as calculated using the Pythagoras theorem.
D.None of these
Answer» B. The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them

345.Euclidean distance measure is
A.A stage of the KDD process in which new data is added to the existing selection.
B.The process of finding a solution for a problem simply by enumerating all possible solutions according to some pre-defined order and then testing them.
C.The distance between two points as calculated using the Pythagoras theo- rem
D.None of these
Answer» C. The distance between two points as calculated using the Pythagoras theo- rem

346.Heuristic is
A.A set of databases from different vendors, possibly using different database paradigms
B.An approach to a problem that is not guaranteed to work but performs well in most cases.
C.Information that is hidden in a database and that cannot be recovered by a simple SQL query.
D.None of these
Answer» B. An approach to a problem that is not guaranteed to work but performs well in most cases.

347.Heterogeneous databases referred to
A.A set of databases from different vendors, possibly using different database paradigms
B.An approach to a problem that is not guaranteed to work but performs well in most cases.
C.Information that is hidden in a database and that cannot be recovered by a simple SQL query.
D.None of these
Answer» A. A set of databases from different vendors, possibly using different database paradigms

348.Hidden knowledge referred to
A.A set of databases from different vendors, possibly using different database paradigms
B.An approach to a problem that is not guaranteed to work but performs well in most cases.
C.Information that is hidden in a database and that cannot be recovered by a simple SQL query.
D.None of these
Answer» C. Information that is hidden in a database and that cannot be recovered by a simple SQL query.

349.Hybrid is
A.Combining different types of method or information
B.Approach to the design of learning algorithms that is structured along the lines of the theory of evolution.
C.Decision support systems that contain an Information base filled with the knowledge of an expert formulated in terms of if-then rules.
D.None of these
Answer» A. Combining different types of method or information

351.Expert systems
A.Combining different types of method or information
B.Approach to the design of learning algorithms that is structured along the lines of the theory of evolution.
C.Decision support systems that contain an Information base filled with the knowledge of an expert formulated in terms of if-then rules
D.None of these
Answer» C. Decision support systems that contain an Information base filled with the knowledge of an expert formulated in terms of if-then rules

352.Extendible architecture is
A.Modular design of a software application that facilitates the integration of new modules
B.Showing a universal law or rule to be invalid by providing a counter example
C.A set of attributes in a database table that refers to data in another table
D.None of these
Answer» A. Modular design of a software application that facilitates the integration of new modules

353.Falsification is
A.Modular design of a software application that facilitates the integration of new modules
B.Showing a universal law or rule to be invalid by providing a counter example
C.A set of attributes in a database table that refers to data in another table
D.None of these
Answer» B. Showing a universal law or rule to be invalid by providing a counter example

354.Foreign key is
A.Modular design of a software application that facilitates the integration of new modules
B.Showing a universal law or rule to be invalid by providing a counter example
C.A set of attributes in a database table that refers to data in another table
D.None of these
Answer» C. A set of attributes in a database table that refers to data in another table

355.Hybrid learning is
A.Machine-learning involving different techniques
B.The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned
C.Learning by generalizing from examples
D.None of these
Answer» A. Machine-learning involving different techniques

356.Incremental learning referred to
A.Machine-learning involving different techniques
B.The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned
C.Learning by generalizing from examples
D.None of these
Answer» B. The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned

357.Information content is
A.The amount of information with in data as opposed to the amount of redundancy or noise
B.One of the defining aspects of a data warehouse
C.Restriction that requires data in one column of a database table to the a sub- set of another-column.
D.None of these
Answer» A. The amount of information with in data as opposed to the amount of redundancy or noise

358.Inclusion dependencies
A.The amount of information with in data as opposed to the amount of redundancy or noise
B.One of the defining aspects of a data warehouse
C.Restriction that requires data in one column of a database table to the a sub- set of another-column
D.None of these
Answer» C. Restriction that requires data in one column of a database table to the a sub- set of another-column

359.KDD (Knowledge Discovery in Databases) is referred to
A.Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A)
B.Set of columns in a database table that can be used to identify each record within this table uniquely.
C.collection of interesting and useful patterns in a database
D.none of these
Answer» A. Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A)

360.Key is referred to
A.Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A)
B.Set of columns in a database table that can be used to identify each record within this table uniquely
C.collection of interesting and useful patterns in a database
D.none of these
Answer» B. Set of columns in a database table that can be used to identify each record within this table uniquely

361.Inductive learning is
A.Machine-learning involving different techniques
B.The learning algorithmic analyzes the examples on a systematic basis and makes incremental adjustments to the theory that is learned
C.Learning by generalizing from examples
D.None of these
Answer» C. Learning by generalizing from examples

362.Integrated is
A.The amount of information with in data as opposed to the amount of redundancy or noise
B.One of the defining aspects of a data warehouse
C.Restriction that requires data in one column of a database table to the a sub- set of another-column.
D.None of these
Answer» B. One of the defining aspects of a data warehouse

363.Knowledge engineering is
A.The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
B.It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks.
C.A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out.
D.None of these
Answer» A. The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system

364.Kohonen self-organizing map referred to
A.The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
B.It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks
C.A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out.
D.None of these
Answer» B. It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks

365.Learning is
A.The process of finding the right formal representation of a certain body of knowledge in order to represent it in a knowledge-based system
B.It automatically maps an external signal space into a system’s internal representational space. They are useful in the performance of classification tasks.
C.A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out.
D.None of these
Answer» C. A process where an individual learns how to carry out a certain task when making a transition from a situation in which the task cannot be carried out to a situation in which the same task under the same circumstances can be carried out.

366.Learning algorithm referrers to
A.An algorithm that can learn
B.A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
C.A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning.
D.None of these
Answer» A. An algorithm that can learn

367.Meta-learning is
A.An algorithm that can learn
B.A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
C.A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning.
D.None of these
Answer» C. A machine-learning approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning.

368.Machine learning is
A.An algorithm that can learn
B.A sub-discipline of computer science that deals with the design and implementation of learning algorithms.
C.An approach that abstracts from the actual strategy of an individual algorithm and can therefore be applied to any other form of machine learning.
D.None of these
Answer» B. A sub-discipline of computer science that deals with the design and implementation of learning algorithms.

369.Inductive logic programming is
A.A class of learning algorithms that try to derive a Prolog program from examples*
B.A table with n independent attributes can be seen as an n- dimensional space.
C.A prediction made using an extremely simple method, such as always predicting the same output.
D.None of these
Answer» A. A class of learning algorithms that try to derive a Prolog program from examples*

370.Multi-dimensional knowledge is
A.A class of learning algorithms that try to derive a Prolog program from examples
B.A table with n independent attributes can be seen as an n- dimensional space
C.A prediction made using an extremely simple method, such as always predicting the same output.
D.None of these
Answer» B. A table with n independent attributes can be seen as an n- dimensional space

371.Naive prediction is
A.A class of learning algorithms that try to derive a Prolog program from examples
B.A table with n independent attributes can be seen as an n- dimensional space.
C.A prediction made using an extremely simple method, such as always predicting the same output.
D.None of these
Answer» C. A prediction made using an extremely simple method, such as always predicting the same output.

372.Knowledge is referred to
A.Non-trivial extraction of implicit previously unknown and potentially useful information from dat(A)
B.Set of columns in a database table that can be used to identify each record within this table uniquely.
C.collection of interesting and useful patterns in a database
D.none of these
Answer» C. collection of interesting and useful patterns in a database

373.Node is
A.A component of a network
B.In the context of KDD and data mining, this refers to random errors in a database table.
C.One of the defining aspects of a data warehouse
D.None of these
Answer» A. A component of a network

374.Projection pursuit is
A.The result of the application of a theory or a rule in a specific case
B.One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table.
C.Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces
D.None of these
Answer» C. Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces

376.Prediction is
A.The result of the application of a theory or a rule in a specific case
B.One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table.
C.Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces.
D.None of these
Answer» A. The result of the application of a theory or a rule in a specific case

377.Primary key is
A.The result of the application of a theory or a rule in a specific case
B.One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table
C.Discipline in statistics that studies ways to find the most interesting projections of multi-dimensional spaces.
D.None of these
Answer» B. One of several possible enters within a database table that is chosen by the designer as the primary means of accessing the data in the table

378.Noise is
A.A component of a network
B.In the context of KDD and data mining, this refers to random errors in a database table.
C.One of the defining aspects of a data warehouse
D.None of these
Answer» B. In the context of KDD and data mining, this refers to random errors in a database table.

379.Quadratic complexity is
A.A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A)
B.Attributes of a database table that can take only numerical values.
C.Tools designed to query a database.
D.None of these
Answer» A. A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A)

380.Query tools are
A.A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A)
B.Attributes of a database table that can take only numerical values.
C.Tools designed to query a database.
D.None of these
Answer» C. Tools designed to query a database.

381.Prolog is
A.A programming language based on logic
B.A computer where each processor has its own operating system, its own memory, and its own hard disk.
C.Describes the structure of the contents of a database.
D.None of these
Answer» A. A programming language based on logic

382.Massively parallel machine is
A.A programming language based on logic
B.A computer where each processor has its own operating system, its own memory, and its own hard disk
C.Describes the structure of the contents of a database.
D.None of these
Answer» B. A computer where each processor has its own operating system, its own memory, and its own hard disk

383.Meta-data is
A.A programming language based on logic
B.A computer where each processor has its own operating system, its own memory, and its own hard disk.
C.Describes the structure of the contents of a database
D.None of these
Answer» C. Describes the structure of the contents of a database

384.n(log n) is referred to
A.A measure of the desired maximal complexity of data mining algorithms
B.A database containing volatile data used for the daily operation of an organization
C.Relational database management system
D.None of these
Answer» A. A measure of the desired maximal complexity of data mining algorithms

385.Operational database is
A.A measure of the desired maximal complexity of data mining algorithms
B.A database containing volatile data used for the daily operation of an organization
C.Relational database management system
D.None of these
Answer» B. A database containing volatile data used for the daily operation of an organization

386.Oracle is referred to
A.A measure of the desired maximal complexity of data mining algorithms
B.A database containing volatile data used for the daily operation of an organization
C.Relational database management system
D.None of these
Answer» C. Relational database management system

387.Paradigm is
A.General class of approaches to a problem.
B.Performing several computations simultaneously.
C.Structures in a database those are statistically relevant.
D.Simple forerunner of modern neural networks, without hidden layers.
Answer» A. General class of approaches to a problem.

388.Patterns is
A.General class of approaches to a problem.
B.Performing several computations simultaneously.
C.Structures in a database those are statistically relevant
D.Simple forerunner of modern neural networks, without hidden layers.
Answer» C. Structures in a database those are statistically relevant

389.Parallelism is
A.General class of approaches to a problem.
B.Performing several computations simultaneously
C.Structures in a database those are statistically relevant.
D.Simple forerunner of modern neural networks, without hidden layers.
Answer» B. Performing several computations simultaneously

390.Perceptron is
A.General class of approaches to a problem.
B.Performing several computations simultaneously.
C.Structures in a database those are statistically relevant.
D.Simple forerunner of modern neural networks, without hidden layers.
Answer» D. Simple forerunner of modern neural networks, without hidden layers.

391.Shallow knowledge
A.The large set of candidate solutions possible for a problem
B.The information stored in a database that can be, retrieved with a single query.
C.Worth of the output of a machine- learning program that makes it under- standable for humans
D.None of these
Answer» B. The information stored in a database that can be, retrieved with a single query.

392.Statistics
A.The science of collecting, organizing, and applying numerical facts
B.Measure of the probability that a certain hypothesis is incorrect given certain observations.
C.One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A)
D.None of these
Answer» A. The science of collecting, organizing, and applying numerical facts

393.Subject orientation
A.The science of collecting, organizing, and applying numerical facts
B.Measure of the probability that a certain hypothesis is incorrect given certain observations.
C.One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A)
D.None of these
Answer» C. One of the defining aspects of a data warehouse, which is specially built around all the existing applications of the operational dat(A)

394.Search space
A.The large set of candidate solutions possible for a problem
B.The information stored in a database that can be, retrieved with a single query.
C.Worth of the output of a machine- learning program that makes it understandable for humans
D.None of these
Answer» A. The large set of candidate solutions possible for a problem

395.Transparency
A.The large set of candidate solutions possible for a problem
B.The information stored in a database that can be, retrieved with a single query.
C.Worth of the output of a machine- learning program that makes it under- standable for humans
D.None of these
Answer» C. Worth of the output of a machine- learning program that makes it under- standable for humans

396.Quantitative attributes are
A.A reference to the speed of an algorithm, which is quadratically dependent on the size of the dat(A)
B.Attributes of a database table that can take only numerical values.
C.Tools designed to query a database.
D.None of these
Answer» B. Attributes of a database table that can take only numerical values.

397.Unsupervised algorithms
A.It do not need the control of the human operator during their execution.
B.An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars.
C.The validation of a theory on the basis of a finite number of examples.
D.None of these
Answer» A. It do not need the control of the human operator during their execution.

398.Vector
A.It do not need the control of the human operator during their execution.
B.An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars.
C.The validation of a theory on the basis of a finite number of examples.
D.None of these
Answer» B. An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars.

399.Verification
A.It does not need the control of the human operator during their execution.
B.An arrow in a multi-dimensional space. It is a quantity usually characterized by an ordered set of scalars.
C.The validation of a theory on the basis of a finite number of examples
D.None of these
Answer» C. The validation of a theory on the basis of a finite number of examples

401.Voronoi diagram
A.A class of graphic techniques used to visualize the contents of a database
B.The division of a certain space into various areas based on guide points.
C.A branch that connects one node to another
D.None of these
Answer» B. The division of a certain space into various areas based on guide points.

402.Synapse is
A.A class of graphic techniques used to visualize the contents of a database
B.The division of a certain space into various areas based on guide points.
C.A branch that connects one node to another
D.None of these
Answer» C. A branch that connects one node to another

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