TAR: temporal association rules on evolving numerical attributes

Abstract

Data mining has been an area of increasing interest. The association rule discovery problem in particular has been widely studied. However, there are still some unresolved problems. For example, research on mining patterns in the evolution of numerical attributes is still lacking. This is both a challenging problem and one with significant practical applications in business, science, and medicine. In this paper we present a temporal association rule model for evolving numerical attributes. Metrics for qualifying a temporal association rule include the familiar measures of support and strength used in traditional association rule mining and a new metric called density. The density metric not only gives us a way to extract the rules that best represent the data, but also provides an effective mechanism to prune the search space. An efficient algorithm is devised for mining temporal association rules, which utilizes all three thresholds (especially the strength) to prune the search space drastically. Moreover, the resulting rules are represented in a concise manner via rule sets to reduce the output size. Experimental results on real and synthetic data sets demonstrate the efficiency of our algorithm.