Generalized Association Rule Mining Using Genetic Algorithms

Abstract

We formulate a general Association rule mining model for extracting useful information from very large databases. An interactive Association rule mining system is designed using a combination of genetic algorithms and a modified a-priori based algorithm. The association rule mining problem is modeled as a multi-objective combinatorial problem which is solved using genetic algorithms. The combination of genetic algorithms with a-priori query optimization make association rule mining yield fast results. In this paper we use the same combination to extend it to a much more general context allowing efficient mining of very large databases for many different kinds of patterns. Given a large database of transactions, where each transaction consists of a set of items, and a taxonomy (is-a hierarchy) on the items, we find associations between items at any level of the taxonomy. We show how the idea can be used either in a general purpose mining system or in a next generation of conventional query optimizers.