Mining weighted association rules for fuzzy quantitative items

Abstract

During the last ten years, data mining, also known as knowledge discovery in databases, has established its position as a prominent and important research area. Mining association rules is one of the important research problems in data mining. Many algorithms have been proposed to find association rules in large databases containing both categorical and quantitative attributes. We generalize this to the case where part of attributes are given weights to reflect their importance to the user. In this paper, we introduce the problem of mining weighted quantitative association rules based on fuzzy approach. Using the fuzzy set concept, the discovered rules are more understandable to a human. We propose two different definitions of weighted support: with and with-out normalization. In the normalized case, a subset of a frequent itemset may not be frequent, and we cannot generate candidate k-itemsets simply from the frequent (k-1)-itemsets. We tackle this problem by using the concept of z-potential frequent subset for each candidate itemset. We give an algorithm for mining such quantitative association rules. Finally, we describe the results of using this approach on a real-life dataset.