Anna University Data Warehousing and Data Mining November December 2011 Question Paper

Description 0 Reg. No. : Question Paper Code : 55279 Seventh Semester 4 Computer Science and Engineering 4 B.E./B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2011. CS 2032 — DATA WAREHOUSING AND DATA MINING (Common to Sixth Semester Information Technology) (Regulation 2008) Maximum : 100 marks 0 Time : Three hours Answer ALL questions. 4 PART A — (10 × 2 = 20 marks) What is a data mart? 2. List the three important issues that have to be addressed during data integration. 4
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    Reg. No. : B.E./B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2011. Seventh Semester Computer Science and Engineering CS 2032 — DATA WAREHOUSING AND DATA MINING (Common to Sixth Semester Information Technology) (Regulation 2008)  Time : Three hours Maximum : 100 marks Answer ALL questions. PART A — (10 ×  2 = 20 marks) 1.   What is a data mart? 2.   List the three important issues that have to be addressed during data integration. 3.   What is a multi dimensional database? 4.   What is an apex cuboid? 5.   State the need for data cleaning. 6.   What is pattern evaluation? 7.   What is correlation analysis? 8.   What is rule based classification? Give an example. 9.   Define clustering. 10.   What is an outlier? Mention its application. PART B — (5 ×  16 = 80 marks) Question Paper Code :  55279     4    4    0                4    4    0                4    4    0  55279   2 11.   (a) What is a data warehouse? With the help of a neat sketch, explain the various components in a data warehousing system. (16) Or (b) What is a multiprocessor architecture? List and discuss the steps involved in mapping a data warehouse to a multiprocessor architecture. (16) 12.   (a) (i) Distinguish between Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). (4) (ii) What is business analysis? List and discuss the basic features that are provided by reporting and query tools used for business analysis. (12) Or (b) Giving suitable examples, describe the various multi-dimensional schema. (16) 13.   (a) (i) List and discuss the classification of data mining systems. (8) (ii) List and discuss the steps for integrating a data mining system with a data warehouse. (8) Or (b) (i) What is the significance of interestingness measures in a data mining system? Give examples. (ii) Describe the issues and challenges in the implementation of data mining systems. 14.   (a) (i) What is classification? With an example explain how support vector machines can be used for classification. (10) (ii) What are the prediction techniques supported by a data mining system? (6) Or (b) Apply the a priori algorithm to the following data set. State and discuss each step in the Apriori algorithm. Assume. (16) Solution :  Trans ID Items Purchased 101 Apple, Orange, Litchi, Grapes 102 Apple, Mango 103 Mango, Grapes, Apple 104 Apple, Orange, Litchi, Grapes 105 Pears, Litchi     4    4    0                4    4    0                4    4    0  55279   3  Trans ID Items Purchased 106 Pears 107 Pears, Mango 108 Apple, Orange, Strawberry, Litchi, Grapes 109 Strawberry, Grapes 110 Apple, Orange, Grapes  The set of items is {Apple, Orange, Strawberry, Litchi, Grapes, Pears, Mango}. Use 0.3 for the minimum support value. 15.   (a) What is grid based clustering? With an example explain an algorithm for grid based clustering. (16) Or (b) Consider five points { } 54321  ,,,,  X  X  X  X  X   with the following coordinates as a two dimensional sample for clustering : ( ) ( ) ( ) ( ) ( ) 2,6;1,5;1,5.1;0,0;5.2,5.0 54321  =====  X  X  X  X  X   Illustrate the K-means partitioning algorithms using the above data set. (16)  –––––––––––     4    4    0                4    4    0                4    4    0
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