An algorithm for mining association rules with weighted minimum supports

Abstract

Most existing algorithms employ a uniform minimum support for mining association rules. Nevertheless, each item in a publication database, even each set of items, is exhibited in an individual period. A reasonable minimum support threshold has to be adjusted according to the exhibition period of each k-itemset. Accordingly, this paper proposes a new algorithm, called WMS, for mining association rules with weighted minimum supports in publication databases. WMS discovers all frequent itemsets which satisfy their individual requirement of minimum support thresholds. WMS applies the group closure property to prune futile itemsets, to reduce the number of candidates generated, and thus to generate the candidate sets efficiently. © 2005 by International Federation for Information Processing.