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Course Code and Title CSL407 : Data Mining and Warehousing (DMW) 1. Course Description Concept of Data mining and warehousing, applications to real life examples. The study of data warehousing and various data mining tools. 3 lectures per week. Credit scheme - (L-T-P-C: 3-0-0-6) 2. Required Background or Pre-requisite: CSL403 : Database Management Systems 3. Detailed Description of the Course Introduction to Data Mining and warehousing, real time applications, scope of mining and warehousing for various applications. (1 week) Data warehousing- Various schema, three-tier architecture, design issues, multidimensional model. (2 week) Datawarehouse development life cycle, Data warehouse analysis, CUBE, ROLL UP and STAR queries. (2 week) Data Warehouse Design - Massive denormalisation, STAR schema design , Data ware house Architecture, OLAP, ROLAP and MOLAP , concepts of Fact and dimension table (2 week) Space Management in Data warehouse - Schemas for storing data in warehouse using different storage structures, B-tree index, hash index, clusters, Bitmap index functional index, domain index, Data partitions. (3 week) Performance and Tuning - Query optimization, memory management, process management. I/o management for Data warehouse. (2 week) Data Mining Tools - Association Rules , A priori Algorithms, Fp-trees Algorithms,Constraints and solution. (1 week) Cluster Analysis – Paradigms , DBSCAN , Cluster algorithms. (1 week) Mining Tools - Decision Trees and applications. (1 week) 4. Text books and/or other required material Data mining - Concepts & Techniques, Jiawei Han, Micheline Kamber, Morgan Kaufmann ,2nd Ed.2006. Oracle 8i Data Warehousing, Michale Corey, Michale Abbey, Tata McGraw Hill Fundamentals of Database Systems, Navathe and Elmasry, Addison Wesley, 2000 Data Mining, Arun Pujari Orient Longman, 2003 5. Course Objectives 1) Identify the scope and necessity of Data Mining & Warehousing for the society. 2) Describe the designing of Data Warehousing so that it can be able to solve the root problems. 3) To understand various tools of Data Mining and their techniques to solve the real time problems. 4) To develop ability to design various algorithms based on data mining tools. 5) To develop further interest in research and design of new Data Mining techniques. 6. Class Schedule Lectures : 3 1-hr lectures per week 7. Contribution of Course to Professional Component Lecture: Students should know about design issues of data warehousing, learn various mining tools, able to identify the real time problems and able to design solution using various mining tools, further take the R&D interest and try to contribute some new methods to the area. 8. Evaluation of Students The instructor uses the following methods: 2 sessional exams, end-semester exam, class test, some real time problems as programming assignments. 9. Relationship of Course Objectives to Program outcomes The coorelation of the COs of the course Artificial Intelligence and the POs are shown in the following table. A ‘H’, ‘M’, or ‘L’ mark on a cell indicates whether the COs have a ‘high’, ‘medium’, or ‘low’ correlation with the corresponding PO on the coloumn. A blank cell inidicates that there is no correlation between the COs to a particular PO. Correlation of COs of Data mining & Warehousing PO 1 PO 2 PO 3 PO 4 H H L PO 5 H PO 6 H PO 7 H PO 8 M PO 9 H PO 10 L PO 11 L PO 12 H