Mining Multiple-Level Association Rules in Large Databases

ÐA 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 alevelcrossingo 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. Index TermsÐData mining, knowledge discovery in databases, association rules, multiple-level association rules, algorithms, performance.