Identifying Relationships in Transactional Data

Abstract

Association rules is the traditional way used to study market basket or transactional data. One drawback of this analysis is the huge number of rules generated. As a complement to Association Rules, Association Rules Network (ARN), based on Social Network Analysis (SNA) has been proposed by several researchers. In this work we study a real market basket analysis problem, available in a Belgian supermarket, using ARNs. We learn ARNs by considering the relationships between items that appear more often in the consequent of the Association Rules. Moreover, we propose a more compact variant of ARNs: the Maximal Itemsets Social Network (MISN). In order to assess the quality of these structures, we compute SNA based metrics, like weighted degree and utility of community.