Multiple Choice Questions.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 2. The data Warehouse is__________. A. read only. B. write only. C. read write only. D. none. ANSWER: A 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 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 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 6. The data is stored, retrieved & updated in ____________. A. OLAP. B. OLTP. C. SMTP. D. FTP. ANSWER: B 7. __________describes the data contained in the data warehouse. A. Relational data. B. Operational data. C. Metadata. D. Informational data. ANSWER: C 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 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 10. ________________ is the specialized data warehouse database. A. Oracle. B. DBZ. C. Informix. D. Redbrick. ANSWER: D 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 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 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 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 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 16. . ______________databases are owned by particular departments or business groups. A. Informational. B. Operational. C. Both informational and operational. D. Flat. ANSWER: B 17. The star schema is composed of __________ fact table. A. one. B. two. C. three. D. four. ANSWER: A 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 19. The key used in operational environment may not have an element of__________. A. time. B. cost. C. frequency. D. quality. ANSWER: A 20. Data can be updated in _____environment. A. data warehouse. B. data mining. C. operational. D. informational. ANSWER: C 21. Record cannot be updated in _____________. A. OLTP B. files C. RDBMS D. data warehouse ANSWER: D 22. The source of all data warehouse data is the____________. A. operational environment. B. informal environment. C. formal environment. D. technology environment. ANSWER: A 23. Data warehouse contains_____________data that is never found in the operational environment. A. normalized. B. informational. C. summary. D. denormalized. ANSWER: C 24. The modern CASE tools belong to _______ category. A. a. analysis. B. b.Development C. c.Coding D. d.Delivery ANSWER: A 25. Bill Inmon has estimated___________of the time required to build a data warehouse, is consumed in the conversion process. A. 10 percent. A. Detail data in single fact table is otherwise known as__________. B. ANSWER: C 30. C. MACRO. organized around important subject areas. qualify. D. D. used to run the business in real time and is based on historical data. flexibility. Snowflake schema. C. Star-snowflake schema. . 20 percent. used to support decision making and is based on current data. An operational system is _____________. 40 percent D. ACID. B. contains only current data. D. ANSWER: D 26. A.B. The biggest drawback of the level indicator in the classic star-schema is that it limits_________. monoatomic data. D. atomic data. D. A. C. ability. 80 percent. C. used to support decision making and is based on historical data. ANSWER: C 29. C. diatomic data. updated by end users. D. A. used to run the business in real time and is based on current data. B. contains numerous naming conventions and formats C. A. B. multiatomic data. B. ANSWER: D 28. MICRO. A. ANSWER: C 31. C. ANSWER: C 27. _______test is used in an online transactional processing environment. quantify. MEGA. B. Star schema. A data warehouse is _____________. ___________ is a good alternative to the star schema. Fact constellation. D. ANSWER: D 34. at least one data mart. A. A. data stored in one operational system in the organization. data that can extracted from numerous internal and external sources. C. data in which changes to existing records do not cause the previous version of the records to be eliminated. A. C. D. ANSWER: B 35. Data scrubbing is _____________. B. D. near real-time updates.ANSWER: B 32. B. D. at least one data mart. B. current data intended to be the single source for all decision support systems. The active data warehouse architecture includes __________ A. C. capturing a subset of the data contained in various decision support systems. all of the above. near real-time updates. B. capturing all of the data contained in various decision support systems. a process to upgrade the quality of data after it is moved into a data warehouse. Transient data is _____________. B. B. data in which changes to existing records cause the previous version of the records to be eliminated. capturing all of the data contained in various operational systems. data stored in the various operational systems throughout the organization. a process to load the data in the data warehouse and to create the necessary indexes. D. The generic two-level data warehouse architecture includes __________. far real-time updates. D. data that are never deleted once they have been added. C. data that are never altered or deleted once they have been added. capturing a subset of the data contained in various operational systems. The extract process is ______. a process to upgrade the quality of data before it is moved into a data warehouse ANSWER: D . ANSWER: A 36. data that can extracted from numerous internal and external sources. ANSWER: C 33. a process to reject data from the data warehouse and to create the necessary indexes. ANSWER: B 37. data that has been selected and formatted for end-user support applications. C. A. Reconciled data is ___________. C. A. ANSWER: A 44. C. many-to-many. ANSWER: C 42. Converting data from one field into multiple fields. Converting data from fields into field. D. ANSWER: A 40. D. To confirm that data exists. B. To analyze data for expected relationships. A. A. B. C. Business Intelligence and data warehousing is used for ________. A. ANSWER: C 43. ANSWER: B 39. To explain some observed event or condition. A. ANSWER: A 41. separating data from one source into various sources of data. B. The type of relationship in star schema is __________________. A. a process to reject data from the data warehouse and to create the necessary indexes. _______________ is the goal of data mining. C. To create a new data warehouse. completely demoralized. a process to upgrade the quality of data after it is moved into a data warehouse. Data transformation includes __________. C. B. joining data from one source into various sources of data. D. Data Mining. . ____________ is called a multifield transformation. D. partially demoralized. B. C. a process to change data from a summary level to a detailed level. one-to-many. B. The load and index is ______________. one-to-one. completely normalized. C.38. partially normalized. A. a process to upgrade the quality of data before it is moved into a data warehouse. D. a process to change data from a detailed level to a summary level. many-to-one. Converting data from one field to one field. A. D. Forecasting. a process to load the data in the data warehouse and to create the necessary indexes. B. Fact tables are ___________. Converting data from double fields into multiple fields. irrelevant attributes. and delete information. C.C. B. Hardware. communication. D. Software. create. C. D. derived attributes. ANSWER: D 45. cooperative change data. B. D. ANSWER: A 47. C. Classification rules are extracted from _____________. A. Dimensionality reduction reduces the data set size by removing ____________. information delivery. ANSWER: D 46. Analysis of large volumes of product sales data. logged change data. C. B. A. The most common source of change data in refreshing a data warehouse is _______. A. C. A. backups and recovery. queryable change data. ANSWER: A 49. information exchange. D. D. branches. data acquisition. decision tree. query optimization. Query tool is meant for __________. B. C. Middle ware. D. B. snapshot change data. D. root node. composite attributes. ANSWER: C 48. ________ are responsible for running queries and reports against data warehouse tables. A. End users. ANSWER: B 50. A. B. All of the above. change. siblings. security management. except__________. The data administration subsystem helps you perform all of the following. ANSWER: B . relevant attributes. D. COBWEB. ANSWER: C 54. B. Comparison. B. D. STING. DBMS. A. OLAP. Sybase. ___________ is a method of incremental conceptual clustering. CORBA. storing large volume of data. DBMS C. ANSWER: A 53. C. B. D. ANSWER: C 52. ANSWER: B 56. unconditional independence. Effect of one attribute value on a given class is independent of values of other attribute is called _________. A. RDBMS B. B. A. A. ANSWER: A 57. Data warehouse architecture is based on ______________. C. ORDBMS. Source data from the warehouse comes from _______________. ________________ is a data transformation process. conditional independence. D. TDS. B. EXTENDED DBMS ANSWER: B 55. SQL Server. decision support.51. EXTENDED RDBMS D. The main organizational justification for implementing a data warehouse is to provide ______. A. class conditional independence. cheaper ways of handling transportation. value independence. RDBMS. ODS. Multidimensional database is otherwise known as____________. . A. C. MDDB. D. C. access to data. A. C. A. C. ANSWER: C 61. SMP stands for _______________. Symmetric Multiprogramming. multiple double dimension. ANSWER: D 58. Relational database. B. Filtering. D. C. ANSWER: C 62. multiple data doubling. __________ are designed to overcome any limitations placed on the warehouse by the nature of the relational data model. ANSWER: A 60. __________ are designed to overcome any limitations placed on the warehouse by the nature of the relational data model. B. generalization. ______________ is data about data. D. A. Operational database. C. Data repository. A. Operational database. B. B. multidimensional databases. D. C. C. D. The technology area associated with CRM is _______________. Relational database. Projection. D. Multidimensional database. multi-dimension doubling. specialization. C. Selection. Data repository. A. Symmetric Multiprocessor. ANSWER: B 63. Symmetric Metaprogramming. personalization. B. ANSWER: C 59. MDDB stands for ___________. summarization. Multidimensional database. D. . Symmetric Microprogramming. A.B. ANSWER: C 65. Historical data. Microdata. Repository.A. EIS stands for ______________. Information directory. Executive interface system. A. B. A. ANSWER: A 67. ANSWER: D 69. . Extended interface system. D. ____________ are some popular OLAP tools. D. D. A. A. C. domain consistency. ANSWER: A 68. deduplication. Visual Basic. Informix. disambiguation. Extendable information system. ___________ is data collected from natural systems. Digital directory. ANSWER: A 64. C. ODS data. C. Oracle Express. A. Multidata. Minidata. D. ___________ is an important functional component of the metadata. Metadata. Metacube. D. segmentation. Essbase. B. Oracle. B. B. ANSWER: C 66. B. B. Statistical data. Executive information system. Sybase. D. Data dictionary. C. HOLAP. MRI scan. SQL Server. B. C. C. C. _______________ is an example of application development environments. A. The term that is not associated with data cleaning process is ______. C. A. MOLAP. ANSWER: B 72. cost-sensitive. D. C. ANSWER: B 74. A. D. Association. technical-sensitive. retrospective. interrogative. visualization. D. A. A. Ralph Kimball. predictive. C. C. ANSWER: B 76. ____________ proposed the approach for data integration issues. OLAP. D. alerts. ANSWER: A 70. time-sensitive. _____________ is a process of determining the preference of customer's majority. B. ANSWER: C 71. Ralph Campbell. B. B. John Raphlin. Exceptional reporting in data warehousing is otherwise called as __________. James Gosling. . data mart. Capability of data mining is to build ___________ models. C. C. exception. B.D. B. Strategic value of data mining is ______________. work-sensitive. errors. decision tree. Segmentation. Preferencing. ANSWER: C 73. B. A. ____________ is a metadata repository. D. A. imperative. Classification. bugs. The terms equality and roll up are associated with ____________. ANSWER: C 75. D. C. B. D. D. C. Knowledge discovery in database. D. Knowledge data house. Information collection. A. D. A. A. Business directory. ____________ contains information that gives users an easy-to-understand perspective of the information stored in the data warehouse. ANSWER: B 81. The first International conference on KDD was held in the year _____________. ANSWER: A 77. 1997. 1995. C. C. The full form of KDD is _________. A. . Removing duplicate records is a process called _____________. Design. COBWEB. D. A. B. ANSWER: C 80. 1994. Prism solution directory manager. Technical metadata. Knowledge data definition. Operational metadata. Knowledge database. STUNT. ANSWER: B 79. data cleaning. Analysis. _______________ helps to integrate.A. data cleansing. Business metadata. Financial metadata. B. data pruning. A. B. C. B. C. ANSWER: D 78. maintain and view the contents of the data warehousing system. ANSWER: A 82. ________________ is an expensive process in building an expert system. D. CORBA. B. Study. 1996. recovery. Productivity system. . five. inter-entry data mart. transformation or propagation tools. The power of self-learning system lies in __________. intra-entry data mart. Automated system. A. two. B. C. A. segmentation. B. ANSWER: D 84. enabling it to carry out new tasks. cost. four. D. D. independent data mart. B. ANSWER: A 88. accuracy. C. Discovery of cross-sales opportunities is called ________________. D. B. D. propagation tools only. ANSWER: B 83. C. extraction tools. B. Database. Data dictionary. C. transformation tools only. correction. ANSWER: C 87. Data marts that incorporate data mining tools to extract sets of data are called ______. ____________ can generate programs itself. three. A. visualization. C. How many components are there in a data warehouse? A. simplicity. C. association. D. A.B. ANSWER: B 85. Decision making system. B. Information directory. dependent data marts. ANSWER: D 86. D. C. A. speed. D. Building the informational database is done with the help of _______. Self-learning system. Current detail data. Current detail data. Pen drive. B. C. A. . B. Lightly summarized data. Older detail data. ANSWER: D 90. ANSWER: B 91. the mapping from the operational environment to the data warehouse. Which of the following is not a old detail storage medium? A. Metadata. compact. C. D. Lightly summarized data. Current detail data. D. A. all of the above. A. ANSWER: C 93. A. Highly summarized data. D. ANSWER: D 95. Component Key. B. D. Microfinche. C. Highly summarized data is _______. C. Which of the following is not a component of a data warehouse? A. A directory to help the DSS analyst locate the contents of the data warehouse is seen in ______. ANSWER: A 92. D. compact and hardly accessible. Phot Optical Storage. Lightly summarized data. B. The data from the operational environment enter _______ of data warehouse. B. Metadata. the structure of the data. the algorithms used for summarization. compact and expensive. Older detail data. A. C. C. compact and easily accessible.ANSWER: D 89. RAID. Metadata. B. D. ANSWER: D 94. Metadata contains atleast _________. ________ is data that is distilled from the low level of detail found at the current detailed leve. facts. Older detail data. D. Additivity. D. archieved. Transaction. A. Which of the following is not a primary grain in analytical modeling? A. both A and B. transformation and summarization. purge. All of the above. C. ANSWER: B 100. D. Periodic snapshot.B. A. B. entities. level. number of parts to a key. C. all of the above. The granularity of the fact is the _____ of detail at which it is recorded. C. Highly summarized data. ANSWER: B 98. B. Granularity. The data in current detail level resides till ________ event occurs. . D. ANSWER: C 99. The dimension tables describe the _________. ANSWER: D 97. A. summarization. Accumulating snapshot. granularity of those parts. B. C. C. units of measures. keys. B. D. C. none of the above. summarization. transformation. ANSWER: A 96. Granularity is determined by ______. B. A. D. Lightly summarized data. B. ___________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity. ANSWER: C 101. A. B. fully additive fact. None of the above. D. C. ____________ of data means that the attributes within a given entity are fully dependent on the entire primary key of the entity. it is additive over every dimension of its dimensionality. Granularity. None of the above. Dimensionality. D. . not additive over any dimension. C. non-additive fact. B. ANSWER: B 107. not additive over any dimension. A fact is said to be partially additive if ___________. A fact is said to be fully additive if ___________. A fact representing cumulative sales units over a day at a store for a product is a _________. additive over atleast one but not all of the dimensions. Non-additive measures can often combined with additive measures to create new _________. D. ANSWER: B 104. non-additive measures. ANSWER: A 106. additive over atleast one but not all of the dimensions. B. C. B. ANSWER: A 103. D. A fact is said to be non-additive if ___________. B. Functional Dependency. additive fact. A. A.C. C. C. All of the above. A. ANSWER: C 105. not additive over any dimension. additive over atleast one but not all of the dimensions. A. D. D. ANSWER: C 102. None of the above. Additivity. it is additive over every dimension of its dimensionality. additive measures. A. C. partially additive fact. it is additive over every dimension of its dimensionality. B. partially additive. A. Functional dependency. ANSWER: B 110. A. Classification. A predictive model makes use of ________. Clustering. C. Data driven discovery. Descriptive. Association rules. Dependency. Deductive learning. Summarization. ANSWER: A 112. ANSWER: D 109. __________ is used to map a data item to a real valued prediction variable. C. B. assumptions. Which of the following is a predictive model? A. Regression. . ANSWER: C 111. D. Regression. Classification. Which of the following is a descriptive model? A. Association rules. C. C. current data. D. D. A ___________ model identifies patterns or relationships. ANSWER: B 113. B. Time series analysis. A. Exploratory data analysis. Prediction. B. B.D. C. Predictive. both current and historical data. B. D. ANSWER: D 114. A. Which of the following is the other name of Data mining? A. D. Time series analysis C. historical data. ANSWER: C 108. Sequence discovery. Regression. B. ____________ maps data into predefined groups. All of the above. D. Regression. Prediction. association rules. Time series analysis. B. Information. Data. C. Time series analysis. In ____________. C. A. Information. D. Classification. B. The output of KDD is __________. Data. D. The KDD process consists of ________ steps. Query. Prediction. ANSWER: D 120. ANSWER: C 117. both A & B. Sequence discovery. four. C. Link Analysis is otherwise called as ___________. Clustering. ANSWER: B 115. Process. three. Association rules. Prediction. A. C. D. five. A. D. A. Regression. . D. B. Summarization. Prediction. In ________ the groups are not predefined. B. ANSWER: B 116. B.A. ANSWER: A 119. C. A. C. Regression. C. the value of an attribute is examined as it varies over time. B. A. Query. Useful information. D. affinity analysis. ANSWER: C 118. _________ is a the input to KDD. B. C. Graphical. Various visualization techniques are used in ___________ step of KDD. interpretation.D. preprocessing. selection. Geometric. ANSWER: A . ANSWER: A 125. D. __________ is used to proceed from very specific knowledge to more general information. Compression. A. C. Treating incorrect or missing data is called as ___________. B. interpretation. D. data mining. six. A. All of the above. Approximation. B. dimensionality reduction. D. Box plot and scatter diagram techniques are _______. selection. interpretation. ANSWER: B 126. A. C. Extreme values that occur infrequently are called as _________. preprocessing. Icon-based. A. C. C. outliers. ANSWER: C 123. A. Substitution. selection. transformation. transformation. transformaion. ANSWER: B 122. Induction. B. D. B. A. Pixel-based. C. ANSWER: D 124. B. D. B. ANSWER: C 121. D. Converting data from different sources into a common format for processing is called as ________. rare values. Users. Approximation. does fit in current state. ANSWER: D 132. outliers. some may decrease the efficiency of the algorithm. Compression. D. Administrators. C. Summarization. _______ are needed to identify training data and desired results. Return on Investment. B. D. Approximation. Describing some characteristics of a set of data by a general model is viewed as ____________ A. B.127. . Summarization. missing data. A. ANSWER: C 129. changing data. D. C. does not fit in current state. B. B. ANSWER: B 133. A. C. B. C. A. A. the use of some attributes may simply increase the overall complexity. D. C. B. noisy data. D. The problem of dimensionality curse involves ___________. A. _____________ helps to uncover hidden information about the data. Compression. ANSWER: B 128. Designers. ANSWER: C 130. All of the above. Induction. does not fit in future states. Incorrect or invalid data is known as _________. does fit in future states. ROI is an acronym of ________. the use of some attributes may interfere with the correct completion of a data mining task. Overfitting occurs when a model _________. ANSWER: B 131. A. Programmers. Induction. D. C. B. Standard Query Language. Runtime of Instruction ANSWER: A 134. Space complexity. C. ANSWER: C 136. authenticated use. ANSWER: C 137. A. unauthenticated use. A. Return on Information. D. Standard Quick List. B. Reducing the number of attributes to solve the high dimensionality problem is called as ________. A. Time complexity.B. Transformed. Preprocessed. C. All of the above. A. B. B. Real-world. SQL stand for _________. Structured Query Language. 1980's. dimensionality curse. D. D. ANSWER: B 138. D. Which of the following is not a data mining metric? A. Cleaned. 1970's D. dimensionality reduction. ANSWER: D 139. C. authorized use. 1950's. Structured Query list. unauthorized use. 1960's C. The ____________ of data could result in the disclosure of information that is deemed to be confidential. B. Repetition of Information. C. A. D. . ROI. ANSWER: B 135. C. ___________ data are noisy and have many missing attribute values. B. The rise of DBMS occurred in early ___________. ANSWER: A 143. B. sales promotion strategies. cleaning. C. C. D. marketing strategies. Agrawal et al. B. Data mining helps in __________. ANSWER: C 141. C. support. Data that are not of interest to the data mining task is called as ______. ANSWER: B 145. Toda et al. B. inventory management. C.C. Overfitting. support. A. All of the above. A. support count. A. ANSWER: D 144. B. D. noisy data. B. None of the above. D. ANSWER: C 142. D. The absolute number of transactions supporting X in T is called ___________. Market-basket problem was formulated by __________. A. B. irrelevant data. ANSWER: B 140. Parallelization C. D. confidence. Both A & B. confidence. ______ are effective tools to attack the scalability problem. Sampling. Simon et al. D. The proportion of transaction supporting X in T is called _________. missing data. D. All of the above. C. Steve et al. A. support count. None of the above. ANSWER: C . changing data. A. 66. 20000 transaction contain bread. Which of the following is not a desirable feature of any efficient algorithm? A.66% C. 30% ANSWER: A 148. 3% D. 33. 10000 transaction contain both bread and jam. onset. consequent. support. C. A. 20000 transaction contain bread. C. D. D. antecedent. A. . A. to reduce number of input operations. consequent. Then the confidence of buying bread with jam is _______. precedent. onset. antecedent. 30000 transaction contain jam.33% B. The value that says that transactions in D that support X also support Y is called ______________. 45% D. ANSWER: A 147. D. ANSWER: A 151. B. Then the support of bread and jam is _______.146. to reduce number of output operations. C. A. 20% C. precedent. 50% ANSWER: D 149. B. B. 30000 transaction contain jam. The right hand side of an association rule is called _____. None of the above. support count. B. The left hand side of an association rule is called __________. ANSWER: C 150. 2% B. confidence. 10000 transaction contain both bread and jam. A. 7 If T consist of 500000 transactions. If T consist of 500000 transactions. A. A. FP growth algorithm. level-wise algorithm. A. This is _______. Border set. then it is called ________. border set. This is ___________. A. Downward closure property. B. ANSWER: D 152. Any subset of a frequent set is a frequent set. width-wise algorithm. pincer-search algorithm. D. B. Downward closure property. C. . lattice. D.C. D. maximal frequent set. to have maximal code length. border set. ANSWER: C 156. ANSWER: B 153. B. Upward closure property. C. Upward closure property. C. frequent set. ANSWER: B 155. Maximal frequent set. Maximal frequent set B. If an itemset is not a frequent set and no superset of this is a frequent set. D. C. Any superset of an infrequent set is an infrequent set. Border set. D. D. ANSWER: B 157. If a set is a frequent set and no superset of this set is a frequent set. then it is _______. D. A. All set of items whose support is greater than the user-specified minimum support are called as _____________. Downward closure property. C. A priori algorithm is otherwise called as __________. ANSWER: A 154. infrequent sets. maximal frequent set. C. B. Maximal frequent set. lattice. B. A. Border set. to be efficient in computing. Upward closure property. ANSWER: A 163. The second phaase of A Priori algorithm is ____________. After the pruning of a priori algorithm. C. Candidate generation. D.ANSWER: B 158. Candidate generation. downward. A. Pruning. Pruning. C. B. Itemset eliminations. No border set. _______ will remain. C. breadthwise. bottom-up search. C. Partitioning. ANSWER: C 161. Partitioning. D. top-down search. The a priori frequent itemset discovery algorithm moves _______ in the lattice. The _______ step eliminates the extensions of (k-1)-itemsets which are not found to be frequent. ANSWER: B 162. Pruning. upward. A. D. A. No candidate set. depth first search. Itemset generation. ANSWER: B . ANSWER: A 160. from being considered for counting support. A. B. both upward and downward. The A Priori algorithm is a ___________. B. A. Candidate generation. D. Only candidate set. Itemset generation. C. breadth first search. C. B. B. B. ANSWER: D 159. Only border set. Partitioning. A. D. D. The first phase of A Priori algorithm is _______. C. Bin et al. D. D. A. Argawal et at. Maximum Frequency Control Set. C. ANSWER: C 166. A. B. Solid box. Minimal Frequency Control Set. Box. Certain itemsets in the dashed circle whose support count reach support value during an iteration move into the ______. Solid. B. Dashed. MFCS is the acronym of _____. A. Toda et al. Maximal Frequent Candidate Set. Circle. D. C. ANSWER: A 167. which are essentially . B. increases with the size of the maximum frequent set. decreases with increase in size of the maximum frequent set. Solid. Box. None of the above. B. C. Solid circle. Dashed box. A. ANSWER: A 168. The itemsets in the _______category structures are not subjected to any counting.164. C. ANSWER: C 169. B. decreases with the increase in size of the data. increases with the size of the data. ANSWER: A 165. D. Certain itemsets enter afresh into the system and get into the _______. The number of iterations in a priori ___________. Dynamuc Itemset Counting Algorithm was proposed by ____. Itemsets in the ______ category of structures have a counter and the stop number with them. Dashes. ANSWER: A 170. Minimal Frequent Candidate Set. D. B. Circle. D. C. Simon et at. A. A. ANSWER: D 174. C. four. transformed prefix path. ANSWER: D 171. The itemsets that have completed on full pass move from dashed circle to ________. D. D. Dashed circle. two. two. Solid box. Dashed box. Solid circle. D. A. D. C. A. five. ANSWER: B 173. A. A. B. . three. The paths from root node to the nodes labelled 'a' are called __________. Solid circle. Dashed box. a frequent-item-node. ANSWER: B 176.the supersets of the itemsets that move from the dashed circle to the dashed box. Solid box. both A & B. C. D. The non-root node of item-prefix-tree consists of ________ fields. only one. B. B. A. ANSWER: B 172. three. D. A. C. C. None of the above. C. ANSWER: B 175. an item-prefix-tree. two. A frequent pattern tree is a tree structure consisting of ________. B. four. The frequent-item-header-table consists of __________ fields. a frequent-item-header table. three. B. B. The FP-growth algorithm has ________ phases. A. four. one. __________ clustering techniques starts with all records in one cluster and then try to split that cluster into small pieces. A priori. Classification. with each cluster having only one record. C. D. CLARA. Clustering. Which of the following is a clustering algorithm? A. Partition. D. B. The goal of _____ is to discover both the dense and sparse regions of a data set. C. C. C. ANSWER: C 178. D. Divisive. ANSWER: D 177. Agglomerative. _______ clustering technique start with as many clusters as there are records. C. ANSWER: B 180. FP-tree. Genetic Algorithm. D. . Association rule. B. B. suffix subpath. C. prefix path. A. B.B. D. FP-growth. Partition. The transformed prefix paths of a node 'a' form a truncated database of pattern which co-occur with a is called _______. conditional pattern base. A. Numeric. transformed suffix path. D. suffix path. Agglomerative. A. Pincer-Search. ANSWER: A 181. ANSWER: C 179. prefix subpath. divisive. B. A. Numeric. DBSCAN B. B. CLARANS stands for _______. C. BIRCH. A. D. Clustering Large Applications based on RANdomized Search. A. B. B. STIRR. In ___________ each cluster is represented by one of the objects of the cluster located near the center. CURE.ANSWER: B 182. Which of the following is a data set in the popular UCI machine-learning repository? A. CLARA Net Server. ANSWER: C 186. D. k-means. In ________ algorithm each cluster is represented by the center of gravity of the cluster. D. D. D. ROCK. k-means. ANSWER: A 185. CACTUS. D. A. CLustering Application Randomized Search. Pick out a hierarchical clustering algorithm. CLARA. STIRR. MUSHROOM. ANSWER: B 184. ANSWER: A 187. Pick out a k-medoid algoithm. A. ANSWER: D 183. DBSCAN. CURE. C. BIRCH. C. Clustering Large Application RAnge Network Search. B. PAM. k-medoid. B. C. C. k-medoid. STIRR. C. A. PAM. ANSWER: C . ROCK. Partition. C. D. D. BIRCH is a ________. C. FP tree. ANSWER: A 192. C. B tree. B. A. A. Partition algorithm. A. Association rule. A. ANSWER: A 193. four. The ________ algorithm is based on the observation that the frequent sets are normally very few in number compared to the set of all itemsets. C. The partition algorithm uses _______ scans of the databases to discover all frequent sets. B. divisive. Distributed algorithm. D. A. six. eight. superkey. two. CF tree. candidate. B. ANSWER: A 190. ________is the most well known association rule algorithm and is used in most commercial products. ANSWER: C 189. A priori.188. hierarchical-agglomerative algorithm. Clustering. C. B. The basic idea of the apriori algorithm is to generate________ item sets of a particular size & scans the database. D. primary. C. secondary. hierarchical algorithm. agglomerative clustering algorithm. FP growth tree. B. D. B. A. The cluster features of different subclusters are maintained in a tree called ___________. Apriori algorithm. . ANSWER: D 191. Estimation. Missing. Identification. B. four. C. B. apriori-gen. D. Clarification. D. continuous. one. constant. A. _________data consists of sample input data as well as the classification assignment for the data. D. Illustration. C. ANSWER: B 195. variable. non-continuous. apriori. An algorithm called________is used to generate the candidate item sets for each pass after the first.D. ANSWER: A 194. ___________can be thought of as classifying an attribute value into one of a set of possible classes. B. sampling. A. A. A. ANSWER: B 198. Decision. Verification. Pincer-search algorithm. ___________and prediction may be viewed as types of classification. partition. ANSWER: C 197. D. . B. B. A. A. C. ANSWER: C 199. Prediction can be viewed as forecasting a_________value. C. D. three. ANSWER: B 196. Prediction. C. The basic partition algorithm reduces the number of database scans to ________ & divides it into partitions. two. Estimation. B.B. if-then. Rule based classification algorithms generate ______ rule to perform the classification. A. D. muscles. neurons. ANSWER: D 200. strands. The human brain consists of a network of ___________. D. D. ANSWER: A 201. B. C. Measuring. ANSWER: A 203. Mobile networks. ones. A. Artificial networks. A. The ___________is a long. D. Tissue. cells. while. C. B. dendrites. do while. atoms. switch. single fibre that originates from the cell body. Training. ANSWER: A 205. Neural networks. C. thousands. ANSWER: D 204. axon. Computer networks. . ANSWER: B 202. C. Non-training. A. C. B. A. Each neuron is made up of a number of nerve fibres called _____________. dendrites. neuron. A. ____________ are a different paradigm for computing which draws its inspiration from neuroscience. B. A single axon makes ___________ of synapses with other neurons. hundreds. C. molecules. D. electrons. C. D. B. logistic. probability. D. A. The threshold function is replaced by continuous functions called ________ functions. B. C. regression. B. Receiving process. ANSWER: C 206. C. A. a. Switching process. D. input. C. ANSWER: B 212. Fire. Transmission process. ANSWER: C 210. Biological neurons. C. A. millions. D. B. B. D. A. MLP stands for ______________________. output. ANSWER: C 207. deactivation. Power. The sigmoid function also knows as __________functions. _____________ is a complex chemical process in neural networks. activation.D. write. dynamic. A. Water. _________ is the connectivity of the neuron that give simple devices their real power. Artificial neurons. ANSWER: A 211. ANSWER: D 208. A. B. C. Air. B. C. D. ANSWER: A 209. d. Computational neurons. neural. Technological neurons. b. c. standard. . D. read. The biological neuron's _________ is a continuous function rather than a step function. __________ are highly simplified models of biological neurons. Sending process. bottom. unidirectional. RBF have only _______________ hidden layer.A. C. B. more layer perception. feed busy. C. D. the conncetions between layers are ___________ from input to output. B. feedbackward. RBF stands for _____________. feed free. ___________ training may be used when a clear link between input data sets and target output .forward networks. centre. A. four. that is. A. ANSWER: B 214. ANSWER: A 216. top. The network topology is constrained to be __________________. multi layer perception. In a feed. C. ANSWER: A 215. directional. three. one. C. a particular input value at which they have a maximal output. ANSWER: D 217. B. Radial basis function. ANSWER: D 213. feedforward. C. Radial bi function. D. multidirectional. A. D. many layer perception. D. Radial big function. B. B. A. border. two. RBF hidden layer units have a receptive field which has a ____________. D. D. Radial bio function. ANSWER: C 218. mono layer perception. B. bidirectional. A. C. Unsupervised. D. ___________ employs the supervised mode of learning. ANN. D. XOR. A. B. D. self-organizing map. DR. single organizing map. ANSWER: A 223. C. ANSWER: A 224. SAM. MLP & RBF. self origin map. The actual amount of reduction at each learning step may be guided by _________. A. Competitive. MLP. A. ANSWER: D 219. ANSWER: D 222. ANSWER: D 221. RBF. Supervised. PLM. A. C. D. ________________ design involves deciding on their centres and the sharpness of their Gaussians. ___________ is the most widely applied neural network technique. ____________ is one of the most popular models in the unsupervised framework. A. OSM. B. Perception. MSO. D. ABC. . A. MLP. LMP. B. RBF. C. AND. C. ANSWER: C 220. B. C. B. simple origin map. B. SOM. SOM is an acronym of _______________.values does not exist. D. C. Tomoki Toda. The SOM was a neural network model developed by ________. ANSWER: B 228. C. ANSWER: A . Investment analysis used in neural networks is to predict the movement of _________ from previous data. A. B. A. Simon King. patterns. D. C. C. Julia. A. logical. A. 1980-90. C. D.A. D. stock. B. D. A. 1979 -82. 1970-80. Geo algorithm. ANSWER: B 226. B. Gene algorithm. learning cost. learning time. engines. B. technical. learning rate. models. physical. C. ANSWER: C 225. SOMs are used to cluster a specific _____________ dataset containing information about the patient's drugs etc. C. SOM was developed during ____________. ANSWER: D 227. Genetic algorithm B. B. D. GA stands for _______________. medical. Teuvokohonen. learning level. D. 1990 -60. ANSWER: C 229. General algorithm. D. The mutation operator ______. 1970. A. ANSWER: B 236. Dominance. 1975. ANSWER: A 233. 1965. genetics. The RSES system was developed in ___________. A. B. C. Poland. recombine the population's genetic material. D. D. logistics. Which of the following is an operation in genetic algorithm? A. 1960. ANSWER: C 231. GA was introduced in the year __________. 1940. A. A. ANSWER: A 234. D. ANSWER: A 235. introduce new genetic structures in the population. Inversion. All of the above. C. D. All of the above. B. C. . ANSWER: B 232. B. A. A. Genetic algorithms are search algorithms based on the mechanics of natural_______. recombine the population's genetic material. 1955. B. C. GAs were developed in the early _____________. B. England. Crossover is used to _______. 1950. introduce new genetic structures in the population. America. C. C. 1985. systems.230. B. D. B. to modify the population's genetic material. to modify the population's genetic material. statistics. Italy. Natural Language Page. A. B. B. . A. meta. ANSWER: D 239. C. D. ANSWER: B 240. page. D. Computer network. C. D. multimedia. Physical network. D. B. C. D. Social network. ___________ is a system created for rule induction. . Web structure. LERS. DBS. ANSWER: D 237. Nature Level Program. web. Image structure. Research on mining multi-types of data is termed as _______ data. Non Language Process. Logical network. NLP stands for _________. C. Genetic edge recombination. A. level. B. graphics. B. _________ is the way of studying the web link structure. ANSWER: B 241. Text structure. C. CBS. Web content mining describes the discovery of useful information from the _______contents. ANSWER: B 242. digital. C. A. _______ mining is concerned with discovering the model underlying the link structures of the web. ANSWER: D 238.C. text. Natural Language Processing. RBS. A. D. D. Data structure. B. A. All of the above. D. C. __________ describes the discovery of useful information from the web contents. A. associations. The ________ propose a measure of standing a node based on path counting. Web usage mining. C. In web mining. C. D. link web. Web content mining. ANSWER: B 249. close web. D. pages. _______ is concerned with discovering the model underlying the link structures of the web. A. etc. ANSWER: C 246. open web. associations. classification. In web mining. ANSWER: B 244. clustering. ANSWER: A 245. ANSWER: B 247. D. NNTP . B. Web content mining. _________ is used to know the order in which URLs tend to be accessed. hidden web. _________ is used to know which URLs tend to be requested together. Web structure mining. sequential analysis. clustering.ANSWER: C 243. classification. sequential analysis. B. The ___________ engine for a data warehouse supports query-triggered usage of data A. C. All of the above. D. clustering. ANSWER: A 248. C. All of the above. sequential analysis. A. A. A. C. classification. B. _______ is used to find natural groupings of users. Web structure mining. A. D. B. associations. B. B. In web mining. Web usage mining. SMTP C.B. ________ displays of data such as maps. Obscured D. OLAP D. Concealed ANSWER: B . A. Visual C. Hidden B. POP ANSWER: C 250. charts and other graphical representation allow data to be presented compactly to the users.