Itemset Materializing for Fast Mining of Association Rules

Mining association rules is an important data mining problem. Association rules are usually mined repeatedly in different parts of a database. Current algorithms for mining association rules work in two steps. First, the most frequently occurring sets of items are discovered, then the sets are used to generate the association rules. The first step usually requires repeated passes over the analyzed database and determines the overall performance. In this paper, we present a new method that addresses the issue of discovering the most frequently occurring sets of items. Our method consists in materializing precomputed sets of items discovered in logical database partitions. We show that the materialized sets can be repeatedly used to efficiently generate the most frequently occurring sets of items. Using this approach, required association rules can be mined with only one scan of the database. Our experiments show that the proposed method significantly outperforms the well-known algorithms.