1. Fundamentals of data mining – components of data mining algorithms – Data measurement Strategies – Data quality – Tools for displaying data – Principle Component Analysis – Dealing with uncertaininty – Automation – hypothesis testing.
2. Overview of Data mining algorithms – Tree classifies – Artificial neural Networks – Support vector machines – Association rule mining – Case study.
3. Models and patterns – fundamentals of modeling – Model Structures for production models for probability Distribution and Density functions – Models for Structures – scoring functions – Seeking models with different complexities – Evaluation of models and pattern.
4. Searching for models and patterns – State-space search – Greedy search – parameter optimization methods – EM algorithm – Descriptive modeling probability Distribution- pattern based cluster analysis – Hierarchical clustering – classification modeling – Tree models – Predictive modeling for regression – linear models .
5. Web Data Mining – web content mining – web usage mining – Web Structure mining – Search Engines – Search engine Architecture – Ranking of Web pages – Text retrieval – Image retrieval – time series and sequential retrieval – Case study,
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