Mining Direct and Indirect Association Patterns with Multiple Minimum Supports

Association rules mining is one of the important tasks in data mining research. The key of mining association rules is to find out frequent itemsets based on the user-specified minimum support threshold, which implicitly assumes that all items in the data have similar frequencies. This is often not the case in real-life applications. If the frequencies of items vary a great deal, we will encounter the dilemma called the rare item problem. In this paper, an efficient algorithm to discover association patterns with multiple minimum supports is proposed. The algorithm can not only discover association patterns forming between frequent items, but also discover association rules forming between requent items and rare items or among rare items only. Moreover, an algorithm for mining direct and indirect association patterns with multiple minimum supports is designed simultaneously.