Pattern filtering and classification for market basket analysis with profit-based measures

Abstract

Market basket analysis is one of the typical applications in mining association rules. The valuable information discovered from data mining can be used to support decision making. Generally, support and confidence (objective) measures are used to evaluate the interestingness of association rules. However, in some cases, by using these two measures, the discovered rules may be not profitable and not actionable (not interesting) to enterprises. Therefore, how to discover the patterns by considering both objective measures (e. g. probability) and subjective measures (e. g. profit) is a challenge in data mining, particularly in marketing applications. This paper focuses on pattern evaluation in the process of knowledge discovery by using the concept of profit mining. Data Envelopment Analysis is utilized to calculate the efficiency of discovered association rules with multiple objective and subjective measures. After evaluating the efficiency of association rules, they are categorized into two classes, relatively efficient (interesting) and relatively inefficient (uninteresting). To classify these two classes, Decision Tree (DT)-based classifier is built by using the attributes of association rules. The DT classifier can be used to find out the characteristics of interesting association rules, and to classify the unknown (new) association rules.