Topics session wise
Unit 1: Data Warehouse and OLAP Technology
Session 1. Introduction to Data warehouse and Data Mining course
Session 2. History of Database/Data warehouse/Data Mining Technology, How to retain ROI for the business?
Session 3. Problem with heterogeneous information sources, Data warehouse vs heterogeneous DBMS and what is data warehouse?
Session 4. Difference between OLTP and OLAP, Difference between DBMS, OLAP and Data mining, Types of data warehouse applications.
Session 5. Why seperate data warehouse? Three - tier data warehouse architecture.
Session 6. Typical process flow within a data warehouse, types of data warehouse models, Accessing the data warehouse - Types of tools.
Session 7. OLAP architecture, What is multidimensional data? Different operation in the multidimensional data model.
Session 8. Efficient data cube computation, General strategies for data cube computation.
Session 9. Data cube computation methods: Multiway and BUC.
UNIT-2: Introduction to Data Mining
Session 10. Introduction to Data Mining: What, Why?, A KDD Process
Session 11. Data Mining — Potential Applications
Session 12. Architecture of a Typical Data Mining System, Data Mining: On What Kind of Data?
Session 13. Data Mining Functionalities, Data Mining Tasks
Session 14. Data Mining Task Primitives
Session 15. Major Issues in Data Mining
Session 16. Why Data Preprocessing?, Multi-Dimensional Measure of Data Quality, Major Tasks in Data Preprocessing
Session 17. Data Cleaning, How to Handle Missing Data?, How to Handle Noisy Data?,
Session 18. Data Integration, Data Transformation, Data Reduction Strategies, Discretization and Concept Hierarchy
UNIT-3: Mining Frequent Pattern & Classification
Session 19. Association Rule Mining: Introduction, Different Applications, What Is Association Rule Mining?
Session 20. Association & Correlation, Basic Concepts: Frequent Patterns
Session 21. Rule Measures: Support & Confidence, The apriori Algorithm: Basics
Session 22. Example based on apriori algorithm
Session 23. Mining Frequent Patterns Without Candidate Generation: FP Growth - Example
Session 24. Classification and Prediction: Supervised vs. Unsupervised Learning, Typical Applications, Classification—A Two-Step Process
Session 25. Issues regarding classification and prediction, Classification by Decision Tree Induction
Session 26. Decision Tree Induction: Attribute Selection Measure, example
Session 27. Bayesian Classification: Applications, Bayesian Theorem, Naive Bayes with problem
UNIT-4: Cluster Analysis Introduction
Session 28. Cluster Analysis: Introduction, Applications, What is good clustering
Session 29. Requirements of Clustering in Data Mining, Type of data in clustering analysis
Session 30. Major Clustering Methods, The K-Means Clustering Method
Session 31. The K-Medoids Clustering Method,
Session 32. Problems based on k-mean and k-mediods
Session 33. Problems based on k-mean and k-mediods
Session 34. Hierarchical Clustering: Types, Hierarchical Agglomerative Clustering with problems.
Session 35. More problems on Hierarchical Agglomerative Clustering
Session 36. BIRCH (Balanced Iterative Reducing and Clustering using Hierarchies), Density-Based Clustering Methods
Session 37. DBSCAN with problems, Pros & Cons with DBSCAN
Session 38. More problem on DBSCAN
Session 39. Outlier Discovery
UNIT-5: Pattern Discovery in real world data
Session 40. Mining Time-Series Data, Spatial Data Mining
Session 41. Multimedia Data Mining
Session 42. Text Mining
Session 43. Mining the World Wide Web,
Session 44. Data Mining Applications
Session 45. Data Mining Applications