Section I. Introduction -- 1. Association Rules: An Overview --

Section II. Identifying Interesting Rules -- 2. From Change Mining to Relevance Feedback: A Unified View on Assessing Rule Interestingness -- 3. Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules -- 4. Semantics-Based Classification of Rule Interestingness Measures --

Section III. Post-Analysis and Post-Mining of Association Rules -- 5. Post-Processing for Rule Reduction Using Closed Set -- 6. A Conformity Measure Using Background Knowledge for Association Rules: Application to Text Mining -- 7. Continuous Post-Mining of Association Rules in a Data Stream Management System -- 8. QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules --

Section IV. Rule Selection for Classification -- 9. Variations on Associative Classifiers and Classification Results Analyses -- 10. Selection of High Quality Rules in Associative Classification --

Section V. Visualization and Representation of Association Rules -- 11. Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules -- 12. Visualization to Assist the Generation and Exploration of Association Rules -- 13. Frequent Closed Itemsets Based Condensed Representations for Association Rules --

Section VI. Maintenance of Association Rules and New Forms of Association Rules -- 14. Maintenance of Frequent Patterns: A Survey -- 15. Mining Conditional Contrast Patterns -- 16. Multidimensional Model-Based Decision Rules Mining -- Compilation of References -- About the Contributors -- Index.