Mining Multiple-Level Association Rules in Large Databases

Authors:
Jiawei Han;Yongjian Fu
Affiliations:
-;-
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
1999

Citing 19
Cited 87

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Abstract

A top-down progressive deepening method is developed for efficient mining of multiple-level association rules from large transaction databases based on the Apriori principle. A group of variant algorithms is proposed based on the ways of sharing intermediate results, with the relative performance tested and analyzed. The enforcement of different interestingness measurements to find more interesting rules, and the relaxation of rule conditions for finding 驴level-crossing驴 association rules, are also investigated in the paper. Our study shows that efficient algorithms can be developed from large databases for the discovery of interesting and strong multiple-level association rules.