On discovery of soft associations with “most” fuzzy quantifier for item promotion applications

Abstract

In item promotion applications, there is a strong need for tools that can help to unlock the hidden profit within each individual customer’s transaction history. Discovering association patterns based on the data mining technique is helpful for this purpose. However, the conventional association mining approach, while generating “strong” association rules, cannot detect potential profit-building opportunities that can be exposed by “soft” association rules, which recommend items with looser but significant enough associations. This paper proposes a novel mining method that automatically detects hidden profit-building opportunities through discovering soft associations among items from historical transactions. Specifically, this paper proposes a relaxation method of association mining with a new support measurement, called soft support, that can be used for mining soft association patterns expressed with the “most” fuzzy quantifier. In addition, a novel measure for validating the soft-associated rules is proposed based on the estimated possibility of a conditioned quantified fuzzy event. The new measure is shown to be effective by comparison with several existing measures. A new association mining algorithm based on modification of the FT-Tree algorithm is proposed to accommodate this new support measure. Finally, the mining algorithm is applied to several data sets to investigate its effectiveness in finding soft patterns and content recommendation.