HIT School of business and management science

Data Mining

Data mining covers extracting intresting data from different Databases, Classification of Data Mining Techniques, Data Warehousing: General Principles, Modelling, Design, Implementation, and Optimization, On-Line Analytical Processing, Data Mining Primitives, Languages, and Interfaces, Concept Hierarchies, Description, Statistical Perspectives on Data Mining, Classification and Clustering, Time-Series Analysis, Deviation Detection, Sequential Patterns, Associations and Rule Generation, Genetic Algorithms, Incremental Mining, Scalability issues of Data Mining Algorithms, Visualization of Data Mining Results, High Performance Computing Applications in Data Mining, Case Studies in Mining the Web (document classification, adaptive documents), Predicting Equity Returns from Securities Data and Decision Support Systems.

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