Knowledge Management Books. Post-Mining of Association Rules: Techiques for Effective Knowledge Extraction | by Yanchang Zhao, Chengqi Zhang and Longbing Cao (eds) | 2009 | ISBN: 9781605664040. Multidimensional Model-Based Decision Rules Mining
Post-Mining of Association Rules: Techiques for Effective Knowledge Extraction
by Yanchang Zhao, Chengqi Zhang and Longbing Cao (eds)
2009 (394 pages)
ISBN:9781605664040
Offering tools to extract useful and actionable knowledge after discovering a large number of association rules, this book provides a collection on post-mining, a summarization and presentation of association rules, and new forms of association rules.
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Post-Mining of Association Rules—Techniques for Effective Knowledge Extraction
Foreword
Preface
SUMMARY
BACKGROUND
OBJECTIVES AND SIGNIFICANCE
TARGET AUDIENCES
ORGANIZATION
IMPACTS AND CONTRIBUTIONS
Section I – Introduction
Chapter I – Association Rules—An Overview
ABSTRACT
INTRODUCTION
BACKGROUND
ASSOCIATION RULES
RULE GENERATION
PRUNING
INTERESTINGNESS
POST-MINING
SUMMARY
REFERENCES
Section II – Identifying Interesting Rules
Chapter II – From Change Mining to Relevance Feedback—A Unified View on Assessing Rule Interestingness
ABSTRACT
INTRODUCTION
BACKGROUND
PROBLEMS
ON THE INTERESTINGNESS OF CHANGE
HISTORIES OF ASSOCIATION RULE MEASURES
FRAMEWORK FOR RULE INTERESTINGNESS ASSESSMENT
RULE DISCOVERY
CHANGE ANALYSIS
OBJECTIVE INTERESTINGNESS
SUBJECTIVE INTERESTINGNESS
EVALUATION
FUTURE TRENDS
CONCLUSION
REFERENCES
Chapter III – Combining Data-Driven and User-Driven Evaluation Measures to Identify Interesting Rules
ABSTRACT
INTRODUCTION
BACKGROUND
APPROACH FOR IDENTIFYING INTERESTING RULES USING DATA-DRIVEN AND USER-DRIVEN MEASURES
EXPLORATORY ENVIRONMENT TO AID THE IDENTIFICATION PROCESS OF INTERESTING RULES
CASE STUDY
CONCLUSION AND FUTURE TRENDS
ACKNOWLEDGMENT
REFERENCES
Chapter IV – Semantics-Based Classification of Rule Interestingness Measures
ABSTRACT
INTRODUCTION
RULES AND INTERESTINGNESS MEASURES
CLASSIFICATION OF INTERESTINGNESS MEASURES
COMPARISON WITH RELATED WORKS
CONCLUSION
REFERENCES
ENDNOTES
Section III – Post-Analysis and Post-Mining of Association Rules
Chapter V – Post-Processing for Rule Reduction Using Closed Set
ABSTRACT
INTRODUCTION
RELATED WORK
ASSOCIATION AND CLASSIFICATION RULES
REDUNDANT RULE ELIMINATION
EXPERIMENTAL EVALUATION
CONCLUSION
ACKNOWLEDGMENT
REFERENCES
Chapter VI – A Conformity Measure Using Background Knowledge for Association Rules—Application to Text Mining
ABSTRACT
INTRODUCTION
EXTRACTION OF ASSOCIATION RULES FOR TEXT MINING
CONFORMITY OF AN ASSOCIATION RULE WITH RESPECT TO A KNOWLEDGE MODEL
APPLICATION ON MOLECULAR BIOLOGY CORPUS
DISCUSSION
CONCLUSION AND FUTURE WORK
REFERENCES
ENDNOTES
Chapter VII – Continuous Post-Mining of Association Rules in a Data Stream Management System
ABSTRACT
INTRODUCTION
RELATED WORK
FINDING FREQUENT PATTERNS IN DATA STREAMS
VERIFICATION
MANAGING ASSOCIATION RULES
OTHER MEASURES
VALIDATION WITH HISTORICAL RULE BASE
GENERALIZATION AND SUMMARIZATION
TEMPORAL MINING
WORKFLOW AND DSMS INTEGRATION
FUTURE WORK
CONCLUSION
REFERENCES
Chapter VIII – QROC—A Variation of ROC Space to Analyze Item Set Costs/Benefits in Association Rules
ABSTRACT
INTRODUCTION
BACKGROUND
ROC SPACE
QUALITY ROC: QROC
AN ILLUSTRATIVE EXAMPLE
NEW TRENDS AND RELATED WORK
CONCLUDING REMARKS
REFERENCES
ENDNOTES
Section IV – Rule Selection for Classification
Chapter IX – Variations on Associative Classifiers and Classification Results Analyses
ABSTRACT
INTRODUCTION
ASSOCIATIVE CLASSIFIERS
RULE GENERATION: FROM ASSOCIATION RULES TO CLASSIFICATION RULES
RULE PRUNING: KEEPING THE ESSENTIAL
RULE SELECTION
CHALLENGES AND OPEN PROBLEMS
ANALYZING AND INTERPRETING ASSOCIATIVE CLASSIFIER RESULTS
CONCLUSION
REFERENCES
Chapter X – Selection of High Quality Rules in Associative Classification
ABSTRACT
INTRODUCTION
ASSOCIATIVE CLASSIFICATION
PROPERTIES OF PRUNING TECHNIQUES AND CLASSIFICATION RULES
A REVIEW OF PRUNING METHODS
EXPERIMENTAL RESULTS
CONCLUSION
REFERENCES
Section V – Visualization and Representation of Association Rules
Chapter XI – Meta-Knowledge Based Approach for an Interactive Visualization of Large Amounts of Association Rules
ABSTRACT
INTRODUCTION
RELATED WORKS
BACKGROUND KNOWLEDGE: NOTIONS ON ASSOCIATION RULES
META-KNOWLEDGE BASED INTERACTIVE VISUALIZATION OF (GENERIC) ASSOCIATION RULES
DESCRIPTION OF THE PROPOSED VISUALIZATION TOOLS
GERVIS
EXPERIMENTS
CONCLUSION AND FUTURE WORK
ACKNOWLEDGMENT
REFERENCES
ENDNOTES
Chapter XII – Visualization to Assist the Generation and Exploration of Association Rules
ABSTRACT
INTRODUCTION
SYSTEMATIC SURVEY
AN ITEMSET-DRIVEN CLUSTER-ORIENTED APPROACH
CONCLUSION AND FUTURE TRENDS
ACKNOWLEDGMENT
REFERENCES
Chapter XIII – Frequent Closed Itemsets Based Condensed Representations for Association Rules
ABSTRACT
INTRODUCTION
ASSOCIATION RULE MINING
ASSOCIATION RULES FILTERING METHODS
CONDENSED REPRESENTATIONS
FUTURE TRENDS
CONCLUSION
REFERENCES
ENDNOTES
Section VI – Maintenance of Association Rules and New Forms of Association Rules
Chapter XIV – Maintenance of Frequent Patterns—A Survey
ABSTRACT
INTRODUCTION
PRELIMINARIES AND PROBLEM DESCRIPTION
MAINTENANCE OF FREQUENT PATTERNS
MAINTENANCE ALGORITHMS
PREFIX-TREE-BASED ALGORITHMS
FUTURE OPPORTUNITIES
CONCLUSION
REFERENCES
ENDNOTES
Chapter XV – Mining Conditional Contrast Patterns
ABSTRACT
INTRODUCTION
BACKGROUND: CONCEPTS OF CONDITIONAL CONTRAST PATTERNS
DOMINANCE BEAM RESULTS
REPRESENTATION ISSUES AND RELATIONSHIP WITH CLOSED PATTERNS AND MINIMAL GENERATORS
THE C2PMINER ALGORITHM
EXPERIMENTAL EVALUATION
CONCLUDING REMARKS
ACKNOWLEDGEMENT
REFERENCES
ENDNOTES
Chapter XVI – Multidimensional Model-Based Decision Rules Mining
ABSTRACT
INTRODUCTION
BACKGROUND
PRELIMINARIES
INFORMATION GENERALIZATION
MULTI-LEVEL DECISION RULES MINING
CONCLUSION AND DISCUSSION
FUTURE TRENDS
ACKNOWLEDGMENT
REFERENCES
Compilation of References
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