快速反向關聯法則與調整緊密規則－促銷商品組合之應用

Fast-Backward Association Rule and Update-Compact Rule-Determination of Purchasing Patterns

10.6382/JIM.200307.0181

蔡玉娟(Yuh-Jiuan Tsay)；張簡雅文(Ya-Wen Chang Chien)；黃彥文(Yen-Wen Hung)

資料探勘 ； 關聯法則 ； 快速反向關聯法則 ； Data Mining ； Association Rule ； Fast-Backward Association Rule

資訊管理學報

10卷1期（2003 / 07 / 01）

181 - 204

繁體中文

企業藉由所建立之專屬會員制度，透過資料探勘技術從龐大的會員交易資料庫發掘消費特徵，實現個人化之服務，有效區隔市場與訂定行銷策略。資料探勘技術之關聯法則的執行程序受限於必須由單一項目集，逐層擴展，經過長時間之重複組合與運算步驟，才能發掘合適之高頻項目集。本研究提出一個新的快速反向關聯法則(Fast-Backward Association Rule, FBAR)以克服上述之缺點，並應用於發掘特定促銷項目之商品組合。FBAR之執行程序反向於Apriori關聯法則，執行步驟為：(1)建立促銷目標資料表(table)一婦描交易資料庫一次，將交易資料庫中不符合預定促銷的項目刪除，而保留符合預定促銷的項目，記錄在促銷目標資料表並暫存於主記憶體；(2)分解促銷目標資料表之交易資料－在促銷目標資料表中，由最長交易資料開始逐層分解項目集；(3)發掘符合最小支持度之高頻項目集－當分解至某長度之項目集且已符合最小支持度，則停止該項目集之分解。FBAR僅需掃描資料庫一次，而將刪減之交易資料記錄在促銷目標資料表並暫存於主記憶體，再透過分解較長交易紀錄，可快速發掘符合最小支持度之高頻項目集。藉由調整緊密規則法(Update-Compact Rule, UCR)可轉換高頻項目集為涵蓋率與緊密程度較高之關聯規則。

The discovery of association rules is an important and popular data-mining task, for which many algorithms have been proposed. These solutions are flawed, containing weaknesses that include often requiring repeated passes over the database, and generating a large number of candidate itemsets. In order to overcome the bottlenecks of association rule, we propose a new Fast-Backward Association Rule (FBAR) which is used to discover the specific promotional bundles. The procedure of FBAR is against Apriori algorithm. Efficiency of mining is achieved with three steps: (1) establish promotional table with specific promotional items-delete non-promotional items from database; (2) decompose transactions of promotional table-decomposing from the longest transaction record in promotional table, level by level; (3) find out frequent patterns conform to the minimum support-algorithm terminates when the calculated support is greater than, or equal to, the minimum support. The Update-Compact Rule (UCR) algorithm is obtained by modifying Compact Rule Set to reason the knowledge rules from frequent patterns. FBAR not only prunes considerable amounts of data reducing the time needed to perform data scans and requiring less memory, but also ensures the correctness of the mined results.

1. Agrawal, R.,Imielinski, T.,Swami, A.(1993).Proceedings of the 1993 ACM SIGMOD International Conference on Management of Data.Washington:
2. Agrawal, R.,Srikant, R.(1995).Proceedings of the International Conference on Data Engineering (ICDE).Taipei, Taiwan:
3. Agrawal, R.,Srikant, R.(1994).Proceedings of the 20th International Conference on Very Large Databases.Santiago:Chile.
4. Berry, M. J. A.,Linoff, G. S.(1997).Data Mining Techniques: For Marketing, Sales, and Customer Support.New York, NY:John Wiley ＆ Sons, Inc..
5. Berzal, F.,Cubero, J. C.,Marin, N.,Serrano, J. M.(2000).TBAR: An efficient method for association rule mining in relational databases.Data ＆ Knowledge Engineering,37(1)
6. Brin, S.,Motwani, R.,Silverstein, C.(1997).Proceedings of ACM SIGMOD International Conference on Management of Data.
7. Brin, S.,Motwani, R.,Ullman, J. D.,Tsur, S.(1997).SIGMOD 1997, Proceedings of the ACM-SIGMOD International Conference on Management of Data.Tucson, Arizona:ACM Press.
8. Cabena, P.(1997).Discovering Data Mining: From Concept to Implementation.Prentice Hall.
9. Calantone, R. J.,Sawyer, A. G.(1978).The stability of benefit segments.Journal of Marketing Research,15
10. Carter, C.,Hamilton, H.,Cercone, N.(1997).Principles of Data Mining and Knowledge Discovery.
11. Chen, Ming-Syan,Han, Jiawei,Yu, Philip S.(1996).Data Mining: An Overview from Database Perspective.IEEE Transactions on Knowledge and Data Engineering,8(6)
12. Dunkel, B.,Soparkar, N.(1999).International Conference on Data Engineering.Sydney, Australia:
13. Fu, Y.(1997).Data Mining: Tasks, Techniques, and Applications.IEEE Potentials,16(4)
14. Han, J.(1999).Handbook of Data Mining and Knowledge Discovery.Oxford University Press.
15. Han, J.,Cai, Y.,Cercone, N.(1992).Proceedings of the 1992 International Conference on Very Large Data Bases (VLDB'92).Vancouver, Canada:
16. Han, J.,Fu, Y.(1996).Advances in Knowledge Discovery and Data Mining.AAAI/ MIT Press.
17. Han, J.,Fu, Y.(1995).Proceedings of the 21th International Conference on Very Large Data Bases (VLDB'95).Zich, Switzerland:
18. Han, J.,Pei, J.,Yin, Y.(2000).Proceedings of the 2000 ACM-SIGMOD International Conference Management of Data (SIGMOD'00).Dallas, TX:
19. Han, Jiawei,Kamber, M.(2000).Data Mining: Concepts and Techniques.Morgan Kaufmann Publishers.
20. Liu, Bing,Hsu, Wynne,Ma, Yiming(1999).Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining.
21. Mannila, H.,Ronkainen, P.(1997).Proceedings of the Fourth International Workshop on Temporal Representation and Reasoning (TIME'97).
22. Pork, J. S.,Chen, Ming-Syan,Yu, Philips S.(1995).Proceedings of the ACM SIGMOD International Conference on the Management of Data.
23. Quinlan, J. R.(1986).Induction of Decision Trees.Machine Learning,1(1)
24. Savasere, A.,Omiecinski, E.,Navathe, S.(1995).Proceedings of the 21th International Conference on Very Large Data Bases (VLDB).Zurich, Switzerland:
25. Srikant, R.,Agrawal, R.(1995).Proceedings of the 21th International Conference on Very Large Databases (VLDB).Zurich, Switzerland:
26. Toivonen, H.(1996).22th International Conference on Very Large Databases (VLDB'96).Mumbay, India:
27. Yen, Show-Jane,陳良弼 Chen, Arbee L. P.(1996).The Analysis of Relationships in Databases for Rule Derivation.Journal of Intelligent Information Systems,7(3)
28. 方世榮 Fang, Shyh-Rong(1996)。行銷管理學－分析、計劃、執行、與控制。台北:東華書局。
29. 沈維明 、翁頌舜(1999)。中華民國科技管理論文集。
30. 張希誠(1987)。行銷實務：掌握企業行銷的竅門書。台北:書泉出版社。
31. 蔡秀滿 、陳健民(1999)。第五屆資訊管理暨實務研討會。
32. 蕭富峰(1993)。如何進行促銷。台北:遠流出版社。
33. 魏尚敬(1992)。台灣市場環境行銷管理。中國文化大學出版部。

1. 蔡玉娟、張簡雅文(2005)。矩陣為基礎之關聯法則。資訊管理學報，12(3)，113-130。
2. 龔旭陽、賴威光、林靖祐、林美賢(2010)。針對重要稀少性資料之一種有效率關聯式探勘方法設計。資訊管理學報，17(1)，133-155。
3. 歐益昌、楊雅茹(2011)。應用資料採礦與雲端技術於智慧型稅務選案。數據分析，6(5)，57-76。