Mining substitution rules: a knowledge-based approach using dynamic ontologies

Abstract

Association Rule Mining has so far focused on generating and pruning positive rules using various interestingness measures. However, there are very few studies that explore the mining process of substitution rules. These studies have incorporated a limited definition of substitution, either in statistical terms or based on manager’s static knowledge. Here we attempt to provide a customer-centric model of substitution rule mining using the lens of affordance. We adopt a knowledge-based approach involving a dynamic ontology wherein objects are positioned based on the affordances they are preferred for. This contrasts with the traditional static ontology approach that highlights manager’s static knowledge base. We develop an Expected-Actual Substitution Framework to compare relatedness between items in the static and dynamic ontologies. We present Affordance-Based Substitution (ABS) algorithm to mine substitution rules based on the proposed approach. We also come up with a novel interestingness measure that enhances the quality of our substitution rules thus leading to effective knowledge discovery. Empirical analyses are performed on a real-life supermarket dataset to show the efficacy of ABS algorithm. We compare the generated rules with those generated by another substitution rule mining algorithm from the literature. Our results show that substitution rules generated through ABS algorithm capture customer perceptions that are generally missed by alternate approaches.