Association Rule Mining（关联规则探悉）

Due to the popularity of knowledge discovery and data mining，in practice as well as among academic and corporate R＆D professionals，association rule mining is receiving increasing attention.
　　The authors present the recent progress achieved in mining quantitative association rules，causal rules，exceptional rules，negative association rules，association rules in multi-databases，and association rules in small databases. This book is written for researchers，professionals，and students working in the fields of data mining，data analysis，machine learning，knowledge discovery in databases，and anyone who is interested in association rule mining.

Due to the popularity of knowledge discovery and data mining, in practice as well as among academic and corporate R&D professionals, association rule mining is receiving increasing attention.
The authors present the recent progress achieved in mining quantitative association rules, causal rules, exceptional rules, negative association rules, association rules in multi-databases, and association rules in small databases. This book is written for researchers, professionals, and students working in the fields of data mining, data analysis, machine learning, knowledge discovery in databases, and anyone who is interested in association rule mining.

1.Introduction
1.1 What Is Data Mining？
1.2 Why Do We Need Data Mining？
1.3 Knowledge Discovery in Databases（KDD）
1.4 Data Mining Tadk
1.5 Data Mining Techniques
1.6 Data Mining and Marketing
1.7 Solving Real-World Problems by Data Mining
1.8 Summary
2.Association Rule
2.1 Basic Concepts
2.2 Measurement of Association Rules
2.3 Searching Frequent Itemsets
2.4 Research into Mining Association Rules
2.5 Summary
3.Negative Association Rule
3.1 Introduction
3.2 Focusing on Itemsets of Interest
3.3 Effectiveness of Focusing on Infrequent Itemsets of Interest
3.4 Itemsets of Interest
3.5 Searching Interesting Itemsets
3.6 Negative Association Rules of Interest
3.7 Algorithms Design
3.8 Iden tifying Reliable Exceptions
3.9 Comparisons
3.10 Summary
4.Causality in Databases
4.1 Introduction
4.2 Basic Defintions
4.3 Data Partitioning
4.4 Dependecy among Variables
4.5 Causality in Probabilistic Databases
4.6 Summary
5.Causal Rule Analysis
6.Association Rules in Very Large Databases
7.Association Rules in Small Databases
8.Conclusion and Future Work
References
Subject Index