Koh Y.S., Rountree N. (eds.) Rare Association Rule Mining and Knowledge Discovery. Technologies for Infrequent and Critical Event Detection

IGI Global, 2010, -320 p.This is the third volume of the Advances in Data Warehousing and Mining (ADWM) book series. ADWM publishes books in the areas of data warehousing and mining. This special volume, Rare Association Rule Mining and Knowledge Discovery: Technologies for Infrequent and Critical Event Detection, presents cutting edge research in this newly emerging area. Techniques for rare association mining are quite different from that of traditional rule mining and this book fills an essential gap in this area.
The primary objective of this book is to give readers in-depth knowledge on the current issues in rare association rule mining and critical event detection. The book is designed to cover a comprehensive range of topics related to rare association rule mining and critical event detection: mining techniques, imbalanced datasets, interest metrics, and real-world application domains. We hope this book will highlight the need for growth and research in the area of rare association rule mining and critical event detection. This volume consists of sixteen chapters in four sections.Section 1 Beyond the Support-Confidence Framework
Rare Association Rule Mining: An Overview
Association Rule and Quantitative Association Rule Mining among Infrequent Items
Replacing Support in Association Rule Mining
Effective Mining of Weighted Fuzzy Association Rules
Section 2 Dealing with Imbalanced Datasets
Rare Class Association Rule Mining with Multiple Imbalanced Attributes
A Multi-Methodological Approach to Rare Association Rule Mining
Finding Minimal Infrequent Elements in Multi-Dimensional Data Defined over Partially Ordered Sets and its Applications
Section 3 Rare, Anomalous, and Interesting Patterns
Discovering Interesting Patterns in Numerical Data with Background Knowledge
Mining Rare Association Rules by Discovering Quasi-Functional Dependencies: An Incremental Approach
Mining Unexpected Sequential Patterns and Implication Rules
Mining Hidden Association Rules from Real-Life Data
Strong Symmetric Association Rules and Interestingness Measures
Section 4 Critical Event Detection and Applications
He Wasn’t There Again Today
Filtering Association Rules by Their Semantics and Structures
Creating Risk-Scores in Very Imbalanced Datasets: Predicting Extremely Violent Crime among Criminal Offenders Following Release from Prison
Boosting Prediction Accuracy of Bad Payments in Financial Credit Applications