Research on Association Rules Mining AlgorithmWith Item Constraints

Authors:
Nan Lu;Jing-Zhou Zhou;Wang Zhe;Chun-Guang Zhou
Affiliations:
Shenzhen University, Shenzhen, China;Shenzhen University, Shenzhen, China;Jilin University, Changchun, China;Jilin University, Changchun, China
Venue:
CW '05 Proceedings of the 2005 International Conference on Cyberworlds
Year:
2005

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International Journal of Business Intelligence and Data Mining

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Abstract

The issues in the field of association rules mining with specific items are discussed first. To solve the problems in the ordinary algorithm, we put forward a new but efficient mining algorithm with itemconstraints, called EclatII. We then give an analysis on the performance of the algorithm as well as on its strategy. The experimental result shows that the Eclatll algorithm is more robust in items of using "low support" and "long pattern" association rules than others.