Foundations of Data Mining and knowledge Discovery

From the contents: Part I Foundations of Data Mining Knowledge Discovery as Translation Mathematical Foundation of Association Rules - Mining Associations by Solving Integral Linear Inequalities Comparative Study of Sequential Pattern Mining Models Designing Robust Regression Models A Probabilistic Logic-based Framework for Characterizing Knowledge Discovery in Databases A Careful Look at the Use of Statistical Methodology in Data Mining Justification and Hypothesis Selection in Data Mining.- Part II Methods of Data Mining A Comparative Investigation on Model Selection in Binary Factor Analysis Extraction of Generalized Rules with Automated Attribute Abstraction Decision Making Based on Hybrid of Multi-knowledge and Naive Bayes Classifier First-Order Logic Based Formalism for Temporal Data Mining An Alternative Approach to Mining Association Rules.- Part III General Knowledge Discovery Posting Act Tagging Using Transformation-Based Learning.