Post-Mining Of Association Rules

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There is often a large number of association rules discovered in data mining practice, making it difficult for users to identify those that are of particular interest to them. Therefore, it is important to remove insignificant rules and prune redundancy as well as summarize, visualize, and post-mine the discovered rules. Post-Mining of Association Rules: Techniques for Effective Knowledge Extraction provides a systematic collection on post-mining, summarization and presentation of association rules, and new forms of association rules. This book presents researchers, practitioners, and academicians with tools to extract useful and actionable knowledge after discovering a large number of association rules.

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Product Details

  • ISBN-13: 9781605664040
  • Publisher: IGI Global
  • Publication date: 2/15/2011
  • Pages: 396
  • Product dimensions: 8.70 (w) x 11.20 (h) x 1.20 (d)

Table of Contents

Ch. I Association Rules: An Overview 1

Ch. II From Change Mining to Relevance Feedback: A Unified View on Assessing Rule Interestingness 12

Ch. III Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules 38

Ch. IV Semantics-Based Classification of Rule Interestingness Measures 56

Ch. V Post-Processing for Rule Reduction Using Closed Set 81

Ch. VI A Conformity Measure Using Background Knowledge for Association Rules: Application to Text Mining 100

Ch. VII Continuous Post-Mining of Association Rules in a Data Stream Management System 116

Ch. VIII QROC: A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules 133

Ch. IX Variations on Associative Classifiers and Classification Results Analyses 150

Ch. X Selection of High Quality Rules in Associative Classification 173

Ch. XI Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules 200

Ch. XII Visualization to Assist the Generation and Exploration of Association Rules 224

Ch. XIII Frequent Closed Itemsets Based Condensed Representations for Association Rules 246

Ch. XIV Maintenance of Frequent Patterns: A Survey 273

Ch. XV Mining Conditional Contrast Patterns 294

Ch. XVI Multidimensional Model-Based Decision Rules Mining 311

Compilation of References 335

Index 370

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