Mining multiple-level association rules in large databases

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