Discovering the set of fundamental rule changes
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach
Data Mining and Knowledge Discovery
The Studies of Mining Frequent Patterns Based on Frequent Pattern Tree
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Discovering Periodic-Frequent Patterns in Transactional Databases
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Mining changes in association rules: a fuzzy approach
Fuzzy Sets and Systems
An integrated approach for mining meta-rules
MLDM'05 Proceedings of the 4th international conference on Machine Learning and Data Mining in Pattern Recognition
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Discovering association rules and association rules change (ARC) from existing large databases is an important problem. This paper presents an approach based on multi-hash chain structures to mine association rules change from large database with shorter transactions. In most existing algorithms of association rules change, the mining procedure is divided into two phases, first, association rules sets are discovered using existing algorithm for mining association rules, and then the association rules sets are mined to obtain the association rules change. Those algorithms do not deal with the integration effect to mine association rules and association rules change. In addition, most existing algorithms relate only to the single trend of association rules change. This paper presents an approach which mines both association rules and association rules change and can mine the various trends of association rules change from a multi-hash chain structure. The approach needs only to scan the database twice in the whole mining procedure, so it has lower I/O spending. Experiment results show that the approach is effective to mine association rules using the multi-hash chain structure. The approach has advantages over the Fpgrowth and Apriori algorithm in mining frequent pattern or association rules from large databases with shorter transaction. Besides, the experiment results also show that the approach is effective for mining association rules change and it has better flexibility. The application study indicates the approach can mine and obtain the practicable association rules change.