Data mining is the analysis step of the "knowledge discovery in databases" process, or KDD. Data mining is the extraction of hidden predictive information from large databases is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining methodology extracts hidden predictive information from large databases. With such a broad definition, however, an online analytical processing (OLAP) product or a statistical package could qualify as a data mining tool. Retail companies and the financial community are using data mining to analyze data and recognize trends to increase their customer base, predict fluctuations in interest rates, stock prices, and customer demand.
Week – I 1. Introduction to Data Mining2. Introduction to Data Warehousing3. OLTP4. Trends in Data Warehousing Week – II 5. Applications in Data Warehousing6. Data Warehousing Architecture-I7. Data Warehousing Architecture-II8. Data Warehousing Architecture-III Week – III 9. Data Warehousing Architecture-IV10.Data Warehousing Architecture-V11. Data Mining-I12. Data Mining-II Week – IV 13. Data Mining-III14. Data Mining-IV15. Data Mining-V16. Data Mining-VI Week – V 17. Data Mining-VII18. Association Rule Mining-I19. Association Rule Mining-II Week – VI 20. Association Rule Mining-III21. Association Rule Mining-IV22. Association Rule Mining-V Week – VII 23. Classification Techniques-I24. Classification Techniques-II25. Classification Techniques-III26. Classification Techniques-IV Week – VIII 27. Classification Techniques-V28. Classification Techniques-VI29. Classification Techniques-VII Week – IX30. Other Data Mining Methods-I31. Other Data Mining Methods-II32. Other Data Mining Methods-III33. Other Data Mining Methods-IV Week – X Interaction