Applying Weighted Association Rules with the Consideration of Product Item Relevancy

摘要：

This study aims to introduce a new concept of weighted association rule mining. The purpose is to discover cross section relationship among items and extract the unknown patterns. We proposed two algorithms called HWA (O) and HWA (P) based on the concept that greater the difference among items in an association rule, the higher the weight score is. Hierarchical weights in HWA (O) are assigned according to the hierarchical levels of the items within the itemset and HWA (P) is assigned by a more sophisticated thought proposed. We compared performance of the number of frequent itemsets, number of rules, and content of rules in HWA (O), HWA (P), and Apriori algorithms. As the result, HWA (P) performs better than two other algorithms. At the end of the research, we provide suggestions for retailing managers based on the discovery in the study. To conclude, the algorithm we proposed can efficiently filter out the minor rules and extracts the implicit and unknown patterns. Marketing managers can make decisions more precisely and satisfy customers needs at the same time.

关键词： Data Mining Marketing Retailing Weighted Association Rule

作者: Liewean Cheng Su-Chuan Chen Jashen Chen

作者单位: Department of Information Management,Ta Hwa Institute of Technology,Hsinchu,Taiwan 307 Graduate Institute of Business Administration,Yuan Ze University,Chungli,Taiwan 320 Department of Business Administration,Yuan Ze University,Chungli,Taiwan 320

会议类型: 国际会议

会议名称: 2009 6th International Conference on Service Systems and Service Management( 2009 第六届服务系统与服务管理国际会议)

会议地点: 厦门

会议语种:英文

页码: 888-893

在线出版日期: 2009-06-08（万方平台首次上网日期，不代表论文的发表时间）