Discovering Phenomena-Correlations among Association Rules

Abstract

With the growth of various data types, mining useful association rules from large databases has been an important research topic nowadays. Previous works focus on the attributes of data items to derive a variety of association rules. In this paper, we use the attributes of transactions to organize the data as a multiple-attribute hierarchical tree where the multiple-attribute association rules can be efficiently derived. Furthermore, we store the derived rules as a frequent hierarchical tree and allow users to specify various types of queries for finding interesting correlations named phenomena among the rules. We then make experiments to evaluate the performance of our approach.