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Data Warehousing and Data Mining Time: Three hours Section I : Answer all questions. Section II : Answer all questions. Maximum: 70 marks SECTION I (Covering all 4 Units of syllabus) 1. (5x2M=10M) a b c d e Define Data mining What is Data generalization List out metrics for attribute selection What is the difference between Classification and clustering List out different types of variable 2M 2M 2M 2M 2M SECTION II (Covering 2 successive questions from eachunit of syllabus) (4x15M=60M) Each question can have max.of 2 bits a 2 Explain Different Functionalities of Data mining? 6M b Discuss major issues in Data mining? 15M (or) 3 a Discuss the various issues regarding Preprocessing Data for Classification and Predication b Write about Star and Snowflake data warehousing Schemes 15M 4 a Write short note on Data mining primitives? b Describe DMQL syntax for task-relevant data specification. 15M 5 a How can we perform attribute relevant analysis for concept description? Explain. b Briefly explain about the presentation of class comparison descriptions 15M 6 a Discuss about multi level association rule mining from relational databases.. b Write Apriori algorithm for discovering frequent items sets for mining single dimensional 15M Boolean association rule and discuss various approaches to improve its efficiency (or) (or) 7 a Compare belief network and Naïve Bayes classifier. b With an example, briefly describe the algorithm for generating decision tree to perform 15M classification 8 a What is a Cluster? Briefly describe the categories of clustering techniques b What is Density based clustering? Describe DBSCAN clustering algorithm. 15M a What is text mining? Explain any one approach for text mining? b Briefly discuss about mining the World Wide Web 15M (or) 9
