Mining fuzzy association rules with weighted items

In mast models of mining fuzzy association rules, the items are considered to have equal importance. Due to the diverse human's interestingness and preference to items, such models do not work well in many situations. To improve such models, we propose a method in this paper to mine fuzzy association rules with weighted items. One of the major problems in the research of data mining is the development of good measures of interestingness of discovered rules, in this paper, the weighted support and weighted confidence for fuzzy association rules are defined. The Kohonen self-organized mapping is used to fuzzify the numerical attributes into linguistic terms. A new fuzzy association rules mining algorithm, which generalizes the popular Apriori Gen large itemset based algorithm, is developed. The advantages of the new algorithm are shown by testing it on a census database with 5000 transaction records.