A study on the development of Internet purchase support systems based on data mining and case-based reasoning

In this paper, we introduce the Internet-based purchase support systems using data mining and case-based reasoning (CBR). Internet business activity that involves the end user is undergoing a significant revolution. The ability to track users browsing behavior has brought the vendor and end customers closer than ever before. It is now possible for a vendor to personalize his product message for individual customers at massive scale. Most of our former researchers, in this research arena, used data mining techniques to pursue the customer's future behavior and to improve the frequency of repurchase. The area of data mining can be defined as efficiently discovering association rules from large collections of data. However, the basic association rule-based data mining technique was not flexible. If there were no inference rules to track the customer's future behavior, association rule-based data mining systems may not present more information. To resolve this problem, we combined association rule-based data mining with CBR mechanism. CBR is used in reasoning for customer's preference searching and training through the cases. Data mining and CBR-based hybrid purchase support mechanism can reflect both association rule-based logical inference and case-based information reuse. A Web-log data gathered in the real-world Internet shopping mall is given to illustrate the quality of the proposed systems.