Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
A statistical theory for quantitative association rules
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Discovering associations with numeric variables
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
An evolutionary algorithm to discover numeric association rules
Proceedings of the 2002 ACM symposium on Applied computing
Database Mining: A Performance Perspective
IEEE Transactions on Knowledge and Data Engineering
Mining Optimized Gain Rules for Numeric Attributes
IEEE Transactions on Knowledge and Data Engineering
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Mining Optimized Support Rules for Numeric Attributes
ICDE '99 Proceedings of the 15th International Conference on Data Engineering
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Managing and Mining Uncertain Data
Managing and Mining Uncertain Data
Analysis of sampling techniques for association rule mining
Proceedings of the 12th International Conference on Database Theory
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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Although association rule mining has been studied in the literature for quite a while and numerical attributes are prevalent, perhaps surprisingly, the state-of-the-art quantitative association rule mining is rather inefficient and ineffective in discovering all useful rules. In this paper, we propose a novel divide and conquer two-phase algorithm, which is guaranteed to find all good rules efficiently. We further devise an optimization technique for performance. Moreover, we discuss a few issues with managing and using the discovered quantitative association rules. We perform a comprehensive experimental study which shows that our algorithm is one to two orders of magnitude faster than the state-of-the-art one. In addition, we discover significantly more rules that are useful for prediction.