Post-Mining Of Association Rules
by Yanchang Zhao
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:
… See more details belowOverview
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.
Product Details
- ISBN-13:
- 9781605664040
- Publisher:
- IGI Global
- Publication date:
- 02/15/2011
- 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
Customer Reviews
Average Review: