Module - 1 : Data warehousing and modeling : Basic concepts : Data warehousing : A multitier architecture, Data warehouse models : Enterprise warehouse, Data mart and virtual warehouse, Extraction, Transformation and loading, Data cube : A multidimensional data model, Stars, Snowflakes and fact constellations : Schemas for multidimensional data models, Dimensions : The role of concept hierarchies, Measures : Their categorization and computation, Typical OLAP operations. (Chapter - 1) Module - 2 : Data warehouse implementation and Data Mining : Efficient data cube computation : An overview, Indexing OLAP data : Bitmap index and join index, Efficient processing of OLAP queries, OLAP server architecture ROLAP versus MOLAP versus HOLAP : Introduction : What is data mining, Challenges, Data mining tasks, Data : Types of data, Data quality, Data preprocessing, Measures of similarity and dissimilarity. (Chapter - 2) Module - 3 : Association analysis : Association analysis : Problem definition, Frequent item set generation, Rule generation, Alternative methods for generating frequent item sets, FP - growth algorithm, Evaluation of association patterns. (Chapter - 3) Module - 4 : Classification : Decision trees induction, Method for comparing classifiers, Rule based classifiers, Nearest neighbour classifiers, Bayesian classifiers. (Chapter - 4) Module - 5 : Clustering analysis : Overview, K - means, Agglomerative hierarchical clustering, DBSCAN, Cluster evaluation, Density-based clustering, Graph - based clustering, Scalable clustering algorithms. (Chapter - 5)
