Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection | by Yun Sing Koh and Nathan Rountree | ISBN: 9781605667546. A Multi-Methodological Approach to Rare Association Rule Mining. Mining Unexpected Sequential Patterns and Implication Rules.

March 13, 2010 by kutenk
Filed under: Computer Books 

Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection

by Yun Sing Koh and Nathan Rountree
2010 (319 pages)
ISBN:9781605667546

Covering a comprehensive range of topics, this book provides readers with an in-depth compendium of current issues, trends, and technologies in association rule mining.

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Rare Association Rule Mining and Knowledge Discovery—Technologies for Infrequent and Critical Event Detection







Foreword
Preface
Section 1 – Beyond the Support-Confidence Framework
Chapter 1 – Rare Association Rule Mining—An Overview
ABSTRACT
INTRODUCTION
RARE ITEM PROBLEM
CONCLUSION
FURTHER INFORMATION
REFERENCES




Chapter 2 – Association Rule and Quantitative Association Rule Mining among Infrequent Items
ABSTRACT
INTRODUCTION
BASIC CONCEPTS AND TERMINOLOGY
PRUNING STRATEGIES
CORRELATION ANALYSIS
DISCOVERING ASSOCIATION RULES AMONG INFREQUENT ITEMS
EXPERIMENTAL EVALUATION AND PERFORMANCE STUDY
QUANTITATIVE ASSOCIATION RULE MINING AMONG INFREQUENT ITEMS
CONCLUSION AND FUTURE WORK
REFERENCES

Chapter 3 – Replacing Support in Association Rule Mining
ABSTRACT
INTRODUCTION
RELATED WORK
CONCLUSION
REFERENCES

Chapter 4 – Effective Mining of Weighted Fuzzy Association Rules
ABSTRACT
INTRODUCTION
BACKGROUND AND RELATED WORK
PROBLEM DEFINITION
CONCLUSION
REFERENCES

Section 2 – Dealing with Imbalanced Datasets
Chapter 5 – Rare Class Association Rule Mining with Multiple Imbalanced Attributes
ABSTRACT
INTRODUCTION
RELATED WORKS
RULES WITH IMBALANCED ATTRIBUTES
CONCLUSION
ACKNOWLEDGMENT
REFERENCES

Chapter 6 – A Multi-Methodological Approach to Rare Association Rule Mining
ABSTRACT
INTRODUCTION
RELATED WORK
CONCLUSIONS AND DIRECTIONS FOR FUTURE WORK
REFERENCES

Chapter 7 – Finding Minimal Infrequent Elements in Multi–Dimensional Data Defined Over Partially Ordered Sets and Its Applications
ABSTRACT
INTRODUCTION
DATABASES DEFINED ON PRODUCTS OF PARTIALLY ORDERED SETS
APPLICATIONS
COMPLEXITY
CONCLUSION
REFERENCES

Section 3 – Rare, Anomalous, and Interesting Patterns
Chapter 8 – Discovering Interesting Patterns in Numerical Data with Background Knowledge
ABSTRACT
INTRODUCTION
BACKGROUND
CONCLUSION
REFERENCES




Chapter 9 – Mining Rare Association Rules by Discovering Quasi–Functional Dependencies—An Incremental Approach
ABSTRACT
INTRODUCTION
BACKGROUND
OUTLIER DETECTION
INCREMENTAL OUTLIER DETECTION
EXPERIMENTAL RESULTS
CONCLUSION AND FUTURE WORK
REFERENCES

Chapter 10 – Mining Unexpected Sequential Patterns and Implication Rules
ABSTRACT
INTRODUCTION
RELATED WORK
CONCLUSION
REFERENCES




Chapter 11 – Mining Hidden Association Rules from Real-Life Data
ABSTRACT
INTRODUCTION
BACKGROUND
DEFINITIONS OF ANOMALOUS RULES
RESULTS AND DISCUSSION
FUTURE TRENDS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES

Chapter 12 – Strong Symmetric Association Rules and Interestingness Measures
ABSTRACT
INTRODUCTION
BACKGROUND
STRONG SYMMETRIC ASSOCIATION RULES AND MEASURING INTERESTINGNESS
CONCLUSION
FUTURE WORK
REFERENCES

Section 4 – Critical Event Detection and Applications
Chapter 13 – He Wasn’t There Again Today
ABSTRACT
INTRODUCTION
MARKET BASKETS VS. ADVERSE REACTIONS
CONCLUSION
REFERENCES

Chapter 14 – Filtering Association Rules by Their Semantics and Structures
ABSTRACT
INTRODUCTION
BACKGROUND AND LITERATURE SURVEY
A CASE STUDY
CONCLUSION
REFERENCES

Chapter 15 – Creating Risk-Scores in Very Imbalanced Datasets—Predicting Extremely Violent Crime Among Criminal Offenders Following Release from Prison
ABSTRACT
INTRODUCTION AND BACKGROUND
MEASURING PERFORMANCE IN IMBALANCED DATASETS
ALGORITHMS
FINDING CUT-POINTS ON AUC OPTMIZED RISK SCORES
CONTEXTUAL ISSUES IN CRIMINAL JUSTICE REGARDING VIOLENT CRIME PREDICTION
CASE STUDY
CONCLUSION
REFERENCES

Chapter 16 – Boosting Prediction Accuracy of Bad Payments in Financial Credit Applications
ABSTRACT
INTRODUCTION
CONCLUSION AND FUTURE WORK
REFERENCES

Compilation of References

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One Comment on Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection | by Yun Sing Koh and Nathan Rountree | ISBN: 9781605667546. A Multi-Methodological Approach to Rare Association Rule Mining. Mining Unexpected Sequential Patterns and Implication Rules.

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