Discovery of Multiple-Level Association Rules from Large Databases

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 nding more informative and re ned knowledge from data. In this paper, we study e cient 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 di erent kinds of transaction data. Relaxation of the rule conditions for nding exible multiple-level association rules is also discussed. Our study shows that e cient algorithms can be developed for the discovery of interesting and strong multiple-level association rules from large databases.