Efficient mining of categorized association rules in large databases

A number of studies have been made on discovering association rules in a large database due to the wide applications. The common goal of the studies focused on finding the associated occurrence patterns between all items in a database. In practice, mining the association rules with the granularity as fine as a single item could result in a huge number of rules that are too large to utilize efficiently. In practical applications, the users may be more interested in the associations between the categories the items belong to. In this paper, we propose a new method for mining categorized association rules efficiently by using compressed feature vectors. With the proposed method, at most one scan of the database is needed to produce the categorized association rules in each user query, even under different mining parameters. Furthermore, the calculation time during the mining process is also reduced greatly by using only simple logic operations on feature vectors. Hence, the overall performance in mining categorized association rules could be improved substantially.