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題名：快速反向關聯法則與調整緊密規則--促銷商品組合之應用
書刊名：資訊管理學報
作者：蔡玉娟／張簡雅文／黃彥文
作者(外文)：Tsay, Yuh-jiuan／Chang Chien, Ya-wen／Hung, Yen-wen
出版日期：2003
卷期：10:1
頁次：頁181-204
主題關鍵詞：資料探勘；關聯法則；快速反向關聯法則；Data mining；Association rule；Fast-backward association rule
原始連結：連回原系統網址new window
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  • 被引用次數被引用次數:期刊(2) 博士論文(0) 專書(0) 專書論文(0)
  • 排除自我引用排除自我引用:1
  • 共同引用共同引用:0
  • 點閱點閱:35
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)─掃描交易資料庫一次，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一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，再通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 inc1ude 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.
期刊論文
1.Han, J.、Pei, J.、Yin, Y.、Mao, R.(2004)。Mining frequent patterns without candidate generation: a frequent pattern tree approach。Data Mining and Knowledge Discovery，8(1)，53-87。  new window
2.Park, J. S.、Chen, M. S.、Yu, P. S.(1995)。An effective hash-based algorithm for mining association rules。Association for computing machinery special interest group on management of data，24(2)，175-186。  new window
3.Calantone, R. J.、Sawyer, A. G.(1978)。The Stability of Benefit Segments。Journal of Marketing Research，15(3)，395-404。  new window
4.Chen, Ming-Syan、Han, Jiawei、Yu, Philip S.(1996)。Data Mining: An Overview from a Database Perspective。IEEE Transactions on Knowledge and Data Engineering，8(6)，866-883。  new window
5.Quinlan, J. R.(1986)。Induction of Decision Trees。Machine Learning，1(1)，81-106。  new window
6.Berzal, F.、Cubero, J. C.、Marin, N.、Serrano, J. M.(2001)。TBAR: An Efficient Method for Association Rule Mining in Relational Databases。Data & Knowledge Engineering，37(1)，47-64。  new window
7.Fu, Yongjian(1997)。Data Mining: Tasks, Techniques, and Applications。IEEE Potentials，16(4)，18-20。  new window
8.Yen, Show-Jane、陳良弼(1996)。The Analysis of Relationships in Databases for Rule Derivation。Journal of Intelligent Information Systems，7(3)，235-259。  new window
會議論文
1.Liu B.、Hsu W.、Ma T.(1999)。Mining association rules with multiple minimum supports。ACM SIGKDD International Conference on Knowledge Discovery and Data Mining(KDD-99)。San Diego, USA。337-341。  new window
2.Agrawal, R.、Imielinski, T.、Swami, A. N.(1993)。Mining Association Rules between Sets of Items in Large Databases。The 1993 ACM SIGMOD International Conference on Management of Data，207-216。  new window
3.Han, J.、Cai, Y.、Cercone, N.(1992)。Knowledge discovery in databases: An attribute-oriented approach。The 18th International Conference on Very Large Data Bases。Vancouver。547-559。  new window
4.Han, J.、Fu, Y.(1995)。Discovery of Multiple-level Association Rules from Large Databases。Zürich, Switzerland。420-431。  new window
5.Brin, S.、Motwani, R.、Ullman, J. D.、Tsur, S.(1997)。Dynamic Itemset Counting and Implication Rules for Market Basket Data。The 1997 ACM SIGMOD international conference on Management of data，255-264。  new window
6.Brin, S.、Motwani, R.、Silverstein, C.(1997)。Beyond Market Baskets: Generalizing Association Rules to Correlations。1997 ACM SIGMOD Conference on Management of Data，265-276。  new window
7.Srikant, Ramakrishnan、Agrawal, Rakesh(1995)。Mining Generalized Association Rules。The 21st International Conference on Very Large Data Bases。Zurich。407-419。  new window
8.Srikant, R.、Agrawal, R.(1995)。Mining Sequential Patterns。The Eleventh International Conference on Data Engineering。Taipei：IEEE Computer Society。3-14。  new window
9.Agrawal, R.、Srikant, R.(1994)。Fast algorithms for mining association rules in large database。The 20th International Conference on Very Large Data Bases。Morgan Kaufmann Publishers Inc.。478-499。  new window
10.Omiecinski, E.、Navathe, S.、Savasere, A.(1995)。An Efficient Algorithm for Mining Association Rules in Large Databases。Zürich, Switzerland。432-444。  new window
11.Toivonen, H.(1996)。Sampling Large Databases for Association Rules。Mumbai (Bombay), India。134-145。  new window
12.蔡秀滿、陳健民(1999)。多重資料來源之消費特徵分析。沒有紀錄。  延伸查詢new window
13.Dunkel, B.、Soparkar, N.(1999)。Data Organization and Access for Efficient Data Mining。Sydney, Australia。  new window
14.Mannila, H.、Ronkainen, P.(1997)。Similarity of Event Sequences。沒有紀錄。136-139。  new window
圖書
1.Cabena, P.、Hadjinian, P.、Stadler, R.、Verhees, J.、Zanasi, A.(1997)。Discovering data mining: From concept to implementation。Upper Saddle River, New Jersey：Prentice Hall。  new window
2.Han, Jiawei、Kamber, Micheline(2000)。Data mining: Concepts and techniques。Morgan Kaufmann Publishers。  new window
3.蕭富峰(1993)。如何進行促銷。台北：遠流出版社。  延伸查詢new window
4.Kotler, P.、方世榮(1998)。行銷學管理：分析、計劃、執行與控制。臺北：東華書局。  延伸查詢new window
5.Berry, Michael J. A.、Linoff, Gordon S.(1997)。Data Mining Techniques for Marketing, Sales and Customer Support。John Wiley & Sons, Inc.。  new window
6.沈維明、翁頌舜(1999)。資料挖掘之關聯式法則架構以零售業目標行銷為例。中華民國科技管理論文集。沒有紀錄。  延伸查詢new window
7.張希誠(1987)。行銷實務：掌握企業行銷的竅門書。行銷實務：掌握企業行銷的竅門書。臺北。new window  延伸查詢new window
8.魏尚敬(1992)。臺灣市場環境行銷管理。臺灣市場環境行銷管理。沒有紀錄。  延伸查詢new window
9.Carter, C.、Hamilton, H.、Cercone, N.(1997)。Share Based Measures for Itemsets。Principles of Data Mining and Knowledge Discovery。沒有紀錄。  new window
10.Han, J.(1999)。Characteristic Rules。Handbook of Data Mining and Knowledge Discovery。沒有紀錄。  new window
圖書論文
1.Han, J.、Fu, Y.(1996)。Exploration of the Power of Attribute-Oriented Induction in Data Mining。Advances in Knowledge Discovery and Data Mining。Cambridge, MA：AAAI Press。  new window
 
 
 
 
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