A Post-analysis of the Association Rule Mining Applied to Internee Shopping Mall

Understanding and adapting to changes of customer behavior is an important aspect for a company to survive in continuously changing environment. The aim of this paper is to develop a methodology which detects changes of customer behavior automatically from customer profiles and sales data at different time snapshots. For this purpose, we first define three types of changes as emerging pattern, unexpected change and the added / perished rule. Then we develop similarity and difference measures for rule matching to detect all types of change. Finally, the degree of change is evaluated to detect significantly changed rules. Our proposed methodology can evaluate degree of changes as well as detect all kinds of change automatically from different time snapshot data. A case study for evaluation and practical business implications for this methodology are also provided.