Data Warehousing & Data Mining


Introduction; Dimensional ,Conceptual,Logical, and PhysicalData Models, Data Integrity, Data warehousing and OLAP technology for data mining; Data preprocessing:Data Cleansing, Handling Missing Attribute Values, Discretization Methods, Supervised Methods: Supervised Learning;Classification Trees, Data Mining within a Regression Framework, Rule Induction, Unsupervised Methods: Clustering Algorithms, Association analysis; Frequent Set Mining, Link Analysis, Classification and prediction; Cluster analysis;Soft Computing Methods: Evolutionary Algorithms for Data Mining, Neural Networks For Data Mining, Pattern Clustering, Fuzzy Logic in Data Mining, Statistical Methods, Data Mining Model Comparison, Mining complex types of data; Applications and trends in data mining.

Course Syllabus