Mining association rules in non-transactional databases

Abstract

Association rule mining typically targets transactional data. In order to process non-transaction data in association rule mining, interval identification for each attribute is required. Previous methods perform the interval identification and rule mining steps independently and therefore cannot reflect the change of confidence according to the interval change in discovering association rules. This leads to improper identification of the intervals of attributes, thereby making association rules of high levels of confidence missed in a mining result. In this paper, we propose a novel method to identify good intervals of attributes by performing interval identification and rule mining steps simultaneously. The proposed method adjusts the intervals of attributes during performing the two steps. This makes it possible to find good intervals, and thereby discovering more association rules of high levels of confidence. The proposed method employs hierarchical clustering to group the attributes on the right hand side (RHS) of a rule, and also performs characterization analysis to assess the goodness of each cluster. According to our experimental results with real-world data, the proposed method results in finding useful association rules more than previous methods. Also, the level of confidence of the rules found by our method is greater than those by previous methods.