A matrix algorithm for mining association rules

Abstract

Finding association rules is an important data mining problem and can be derived based on mining large frequent candidate sets. In this paper, a new algorithm for efficient generating large frequent candidate sets is proposed, which is called Matrix Algorithm. The algorithm generates a matrix which entries 1 or 0 by passing over the cruel database only once, and then the frequent candidate sets are obtained from the resulting matrix. Finally association rules are mined from the frequent candidate sets. Numerical experiments and comparison with the Apriori Algorithm are made on 4 randomly generated test problems with small, middle and large sizes. Experiments results confirm that the proposed algorithm is more effective than Apriori Algorithm. © Springer-Verlag Berlin Heidelberg 2005.