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SRM UNIVERSITY FACULTY OF ENGINEERING AND TECHNOLOGY DEPARTMENT OF COMPUTER SCIENCE AND ENGINEERING COURSE PLAN Course Code: CS 0424 Course Title: Data Mining Semester: VIII Course Time: Jan -May 2013 Day A Hour B Timing Hour C Timing Hour D Timing Hour E Timing Hour Timing Day I Day II 3rd 10.35-11.25 3rd 10.35-11.25 3rd 10.35-11.25 3rd 10.35-11.25 3rd 10.35-11.25 1,3 8.45-9.35, 1,3 8.45-9.35, 1,3 8.45-9.35, 1,3 8.45-9.35, 1,3 8.45-9.35, Day III 2 2 9.35-10.25 10.35-11.25 9.35-10.25 10.35-11.25 10.35-11.25 2 9.35-10.25 10.35-11.25 2 9.35-10.25 10.35-11.25 2 9.35-10.25 Location: S.R.M.E.C-Tech Park Faculty Details: Sec A Name Prof.C.MALATHY B Prof .C.Malathy Mr.N.Praveen Mr.N.praveen Mr.V.Deepan C D Ms.S.Nagadevi E Mr.V.Deepan Chakravarthy Office Tech Park Tech Park Tech Park Office Monday to Friday Monday to Friday Monday to Friday Tech Park Tech Park Monday to Friday Monday to Friday Mail id [email protected] [email protected] [email protected] [email protected] "nagadevi s" <[email protected]> [email protected] TEXT BOOKS 1. Margaret H. Dunham, S. Sridhar "Data Mining Introductory & Advance Topics" - 2006(Unit - 1,2,3,4) 2. C.S.R. Prabhu, “Data Warehousing: Concept, Techniques, Products and Applications”, Prentice Hall of India, 2001 (Unit-5). REFERENCE BOOKS 1. J.Han, M.Kamber, “Data Mining: Concept and Techniques”, Academic Press, Morgan Kanfman Publishers, 2001. 2. Pieter Adrians, Dolf Zantinge, “Data Mining”, Addison Wesley, 2000. Prerequisite: NIL Objectives • • • Data mining techniques and algorithms Data Mining environments and applications Spatial Mining, temporal Mining Algorithms Assessment Details Attendance : 5 marks Cycle Test – I Surprise Test – I : : 10 marks 5 marks Cycle Test – II : 10 marks Model Exam : 20 marks Test Schedule S.No. 1 2 3 DATE TEST Cycle Test ‐ I Cycle Test ‐ II Model Exam TOPICS Unit I & II Unit III & IV All 5 units DURATION 2 periods 2 periods 3 Hrs Course outcome To Understand Data mining techniques and algorithms To understand Data Mining environments and applications To Understand problems involved with statistical processing of large databases. Ability to identify associations, classes, and clusters in large data-sets. Program outcome To apply knowledge of computing and mathematics appropriate to the discipline. To use current techniques, skills, and tools necessary for real time systems Detailed Session Plan Introduction: Data Mining Tasks, Data mining Issues, Decision Support System, Dimensional Modeling, Data warehousing, OLAP & its tools, OLTP. Sessi Time Teaching Topics to be covered Ref Testing Method on (min) Method No. 1. Data Mining Tasks T1 PPT/BB Discussion 50 2. Data Mining Tasks 3. Data mining Issues 4. Decision Support System 5. Dimensional Modeling 6 7. 8. Data warehousing 9. OLTP T1 PPT/BB Discussion 50 T1 PPT/BB Test 50 T1 PPT/BB Discussion T1 PPT/BB Viva T1 PPT/BB Discussion T1 PPT/BB Discussion T1 PPT/BB Test 50 50 50 OLAP & its tools 50 50 Mining Techniques: Classification Introduction, statistical Perspective of data mining, Decision tree, Neural networks, Genetic algorithms, Issues in classification, Statistitical based algorithm(regression), Distance based algorithm(simple approach), Decision Tree based algorithm(C4.5), Neural network based (propogation). 10 11. 12. Introduction, statistical Perspective of data mining Decision tree 13. Neural networks 14. Genetic algorithms 50 50 50 T1 PPT/BB Discussion T1 PPT/BB T1 PPT/BB Objective type test Quiz Quiz T1 PPT/BB Objective type test 50 15. Issues in classification 50 T1 PPT/BB Viva 16. Statistitical based algorithm(regression) 50 T1 PPT/BB Discussion 17. Distance based algorithm(simple approach) 50 T1 PPT/BB Discussion 18. Decision Tree based algorithm(C4.5) , Neural network based (propogation). 50 T1 PPT/BB Test Mining Techniques : Clustering and Association Rules Introduction to clustering, Similarity and distance measures, Hierarchical algorithm(divisive clustering), partitional algorithm (Minimum Spanning tree, nearest neighbour), Clustering large database(CURE), Introduction to association, basic algorithm(Apriori), parallel & distributed(data parallelism), Incremental l 19 Introduction i i l to clustering, h i ( Similarity li dand l i l l l) Discussion 50 T1 PPT/BB 20 distance measures 21 Hierarchical algorithm(divisive clustering) Discussion T1 PPT/BB 50 22 23 partitional algorithm (Minimum Spanning tree, nearest neighbour) Clustering large database(CURE) 24 25 26 Introduction to association, basic algorithm(Apriori) parallel & distributed(data parallelism), 50 50 50 50 T1 PPT/BB Test T1 PPT/BB Objective type test T1 PPT/BB Discussion T1 PPT/BB Discussion 27 Incremental rules, Association rule Test T1 PPT/BB 50 techniques(Generalized, multiple level) ADVANCED MINING Web mining, Web content mining, Introduction to Spatial mining & its primitives, spatial classification algorithm (ID3 extension), Spatial clustering algorithm (SD), Introduction to temporal mining, Time series, Temporal association rule. 28 Web mining, Web content mining 29 31 Introduction to Spatial mining & its primitives spatial classification algorithm (ID3 extension), Spatial clustering algorithm(SD), 32 Introduction to temporal mining 30 T1 PPT/BB T1 PPT/BB Group discussion Quiz Objective type test Quiz Quiz T1 PPT/BB Quiz T1 PPT/BB Quiz T1 50 50 50 50 50 PPT/BB Quiz 33 Time series T1 PPT/BB 50 Objective type test 34 35 Temporal association rule. Quiz T1 PPT/BB 50 36 Objective type test DATA MINING ENVIRONMENT Case study in building business environment, Application of data mining in Government National data warehouse and case studies. 37 Case Studies in Building Business Quiz 50 T2 BB 38 Environment 39. 40 41 42 Application of Data Mining – Tamilnadu Government Data Warehouse 50 T2 BB Quiz Objective type test 43 44 45 Data Mining in National Data Warehouse Ministry of Commerce, World Bank 50 T2 BB Group discussion Quiz • • BB – Black Board PP – Power Point Prepared by Approved By C.MALATHY HOD/CSE