Modeling a store's product space as a social network

Abstract

A market basket is a set of products that form a single retail transaction. This purchase data of products can shed important light on how product(s) might influence sales of other product(s). Departing from the standard approach of frequent itemset mining, we posit that purchase data can be modeled as a social network. One can then discover com-munities of products that are bought together, which can lead to expressive exploration and discovery of a larger in-fluence zone of product(s). We develop a novel utility mea-sure for communities of products and show, both financially and intuitively, that community detection provides a useful complement to association rules for market basket analysis. All our conclusions are validated on real store data. © 2009 IEEE.