Data Warehouse basic concepts, Data Warehouse Modeling, Data Cube and OLAP
Introduction, What is Data Mining, Motivating Challenges, Data Mining Tasks,Which technologies are used, which kinds of applications are targeted by Data Mining
Types of Data, Data Mining Applications, Data Preprocessing
Frequent Item set Generation, Rule Generation, Compact Representation of Frequent Itemsets, Alternative methods for generating Frequent Item sets, FP Growth Algorithm,Evaluation of Association Patterns
Basics, G e n e r a l a p p r o a c h t o s o l v e c l a s s i f i c a t i o n problem, D e c i s i o nTrees, R u l e B a s e d Classifiers, Nearest Neighbor Classifiers. Bayesian Classifiers,Estimating Predictive accuracy of classification methods, Improving accuracy ofclarification methods, Evaluation criteria for classification methods, Multiclass Problem.
Overview, Features of cluster analysis, Types of Data and Computing Distance, Types ofCluster Analysis Methods, Partitional Methods, Hierarchical Methods, Density BasedMethods, Quality and Validity of Cluster Analysis
Outlier detection methods, Statistical Approaches, Clustering based applications,Classification based approached