BCA 3/Sem-6/DMDW-Data Mining and Data Warehousing

Chapter 1:- Introduction to Data Mining

  1. Data Mining and Basic Data mining Task
  2.  Data Mining versus Knowledge Discovery in Databases
  3.  Data Mining Issues
  4.  Data Mining Metrics
  5. Applications of Data Mining
  6. Architecture of Data Mining
  7. Data Warehouse, Features of Data Warehouse
  8. Types of Data Warehouse, Applications of Data Warehouse
  9. Architecture of Data Warehousing
  10. Difference between OLTP and OLAP
  11. Types of OLAP-ROLAP,MOLAP,HOLAP
  12. MOLAP Data Cube
  13. MOLAP Operations
  14. Dimensional Data Modeling – star , snowflake schema
  15. Data processing – Need Data cleaning. Data integration and Transformation,Data reduction
  16. Machine Learning and Pattern Matching.

Chapter 2:- Data Mining techniques

  1. Frequent item 
  2. Set and association rule mining-Market Basket Analysis
  3. Apriori algorithm
  4. Frequent item- set tree algorithm
  5. Graph sampling : frequent sub graph mining
  6. Sequence Mining
  7. Classification and Prediction-Model Construction and Model Usage
  8. Decision Tree Induction-Over fitting tree pruning methods
  9. Classification and regression tree(CART)
  10. Bayesians Classification,Bayesians theorem, Bayes Network
  11. Narvee Bayes classifier
  12. Prediction 
  13. Regression-Linear regression and Non-linear regression

Chapter 3:- Clustering

  1. Clustering 
  2. K-means algorithm
  3. Expectation maximization (EM) algorithm and Hierarchical clustering

Chapter 4:- Software for Data mining and application of Data mining

  1. R Software
  2. Weka software