Introduction to the process of knowledge discovery in databases, Basic concepts of data warehousing and data mining, Data preprocessing techniques: selection, extraction, transformation, loading, Data warehouse design and implementation: multidimensional data model, case study using Oracle technology, Overview of data mining process and knowledge discovery, Database Support to Data Mining , Data Mining Techniques and Functions, Cluster Analysis , Regression Algorithms in Data Mining, Neural Networks in Data Mining ,Decision Tree Algorithms.

Machine learning schemes in data mining: finding and describing structure patterns (models) in data, informing future decisions, Information theory and statistics in data mining: from entropy to regression, Data mining core algorithms: statistical modeling, , clustering, association analysis , classification and prediction; Credibility: evaluating what has been leaned from training data and predicting model performance on new data, evaluation methods, and evaluation metrics; Weka: a set of commonly used machine learning algorithms implemented in Java for data

Mining; C5 and Cubist: Decision tree and model tree based data mining tools; Link Analysis in Text Mining, Web Mining Taxonomy, Mining the Web User Behavior, Web Analytics and case studies of real data mining applications.


Introduction to the process of knowledge discovery in databases, Basic concepts of data warehousing and data mining, Data preprocessing techniques: selection, extraction, transformation, loading, Data warehouse design and implementation: multidimensional data model, case study using Oracle technology, Overview of data mining process and knowledge discovery, Database Support to Data Mining , Data Mining Techniques and Functions, Cluster Analysis , Regression Algorithms in Data Mining, Neural Networks in Data Mining ,Decision Tree Algorithms.

Machine learning schemes in data mining: finding and describing structure patterns (models) in data, informing future decisions, Information theory and statistics in data mining: from entropy to regression, Data mining core algorithms: statistical modeling, , clustering, association analysis , classification and prediction; Credibility: evaluating what has been leaned from training data and predicting model performance on new data, evaluation methods, and evaluation metrics; Weka: a set of commonly used machine learning algorithms implemented in Java for data

Mining; C5 and Cubist: Decision tree and model tree based data mining tools; Link Analysis in Text Mining, Web Mining Taxonomy, Mining the Web User Behavior, Web Analytics and case studies of real data mining applications.