Mining Multiple-Level Association Rules in Large Databases

Jiawei Han; Yongjian Fu

doi:10.1109/69.806937

IEEE Transactions on Knowledge and Data Engineering

Mining Multiple-Level Association Rules in Large Databases

Sept.-Oct. 1999, pp. 798-805, vol. 11

DOI Bookmark: 10.1109/69.806937

Authors

Jiawei Han
Yongjian Fu

Keywords

Data Mining, Knowledge Discovery In Databases, Association Rules, Multiple Level Association Rules, Algorithms, Performance

Abstract

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 “level-crossing” 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.

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