Data Warehousing: Introduction, Definition, Multidimensional data transformation, OLAP
operations, Ware house schema, Ware house Server, Other features. Data Mining:
Introduction, Definition, KDD vs. DM, DBMS vs. DM, DM Techniques, Issues and
Challenges in DM, DM Applications. Association Rules: A Prior Algorithm, Partition, Pincer
search, Incremental, Border, FP-tree growth algorithms, Generalized association rule.
Classification: Parametric and non-parametric technology: Bayesian classification, two class and
generalized class classification, classification error, Decision boundary, Discriminate functions,
Non-parametric methods for classification.
Clustering: Hierarchical and non-hierarchical techniques, K-MEDOID Algorithm, Partitioning,
Clara, Clarans. Advanced Hierarchical algorithms
Decision Trees: Decision tree induction, Tree pruning, Extracting classification rules from
decision trees, Decision tree construction algorithms, Decision tree construction with
presorting. Other Techniques for Data mining: Introduction, Learning, Neural Networks, Data
mining using neural networks, Genetic algorithms. Web Mining: Web mining, Text mining,
Content mining, Web structure mining. Searching Techniques: Optimal, non-optimal, Minmax,
H –I pruning.