Considering RFM-values of frequent patterns in transactional databases

Ya Han Hu, Fan Wu, Tzu Wei Yeh

研究成果: 書貢獻/報告類型會議論文篇章同行評審

5 引文 斯高帕斯(Scopus)

摘要

Market basket analysis is an important data mining application for finding correlations between purchasing items in transactional databases. Previous works show that considering constraints which users may concerned with into the mining process can effectively reduce the number of patterns and get more promising information. In this study, we extend the RFM analysis into the mining process to measure the importance of frequent patterns. In RFM analysis, a customer to be recognized as valuable if his/her purchasing records are recent, frequent, and having high amount of money. Follow the same concept of RFM analysis, we first define the RFM-patterns. The RFM-patterns we discovered are not only frequently occurred but also recently bought and having a higher percentage of revenue. After that, we propose a tree structure, named RFMP-tree, to compress and store entire transactional database, and a pattern growth- based algorithm, called RFMP-growth, is developed to discover all RFM-patterns from RFMP-tree. In experimental evaluation, the results show that the algorithm can both significantly reduce the number of discovered patterns and efficiently find the RFM-patterns.

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主出版物標題2nd International Conference on Software Engineering and Data Mining, SEDM 2010
頁面422-427
頁數6
出版狀態已出版 - 2010
事件2nd International Conference on Software Engineering and Data Mining, SEDM 2010 - Chengdu, China
持續時間: 23 6月 201025 6月 2010

出版系列

名字2nd International Conference on Software Engineering and Data Mining, SEDM 2010

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???event.eventtypes.event.conference???2nd International Conference on Software Engineering and Data Mining, SEDM 2010
國家/地區China
城市Chengdu
期間23/06/1025/06/10

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