Discovery of Multiple-Level Association Rules from Large Databases

Abstract

Discovery of association rules from large databases has been a focused topic recently in the research into database mining. Previous studies discover association rules at a single concept level, however, mining association rules at multiple concept levels may lead to finding more informative and refined knowledge from data. In this paper, we study efficient methods for mining multiple-level association rules from large transaction databases. A top-down progressive deepening method is proposed by extension of some existing (single-level) association rule mining algorithms. In particular, a group of algorithms for mining multiple-level association rules are developed and their relative performance are tested on different kinds of transaction data. Relaxation of the rule conditions for finding flexible multiple-level association rules is also discussed. Our study shows that efficient algorithms can be developed for the discovery of interesting and strong multiple-level association rules f...