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題名 Mining Multi-Dimension Rules in Multiple Database Segmentation-on Examples of Cross Selling
作者 吳家齊
Wu,Chia-Chi
貢獻者 姜國輝
CHIANG,Johannes K.
吳家齊
Wu,Chia-Chi
關鍵詞 資料探勘
關聯規則
多維度規則
購物籃分析
data mining
association rule
Multi-dimension rule
Basket analysis
日期 2004
上傳時間 18-九月-2009 14:37:32 (UTC+8)
摘要 在今日以客戶為導向的市場中，“給較好的客戶較好的服務”的概念已經逐漸轉變為“給每一位客戶適當的服務”。藉由跨域行銷(cross-selling)的方式，企業可以為不同的客戶提供適當的服務及商品組合。臺灣的金融業近年來在金融整合中陸續成立了多家金融控股公司，希望藉由銀行、保險與證券等領域統籌資源與資本集中，以整合旗下子公司達成跨領域的共同行銷。這種新的行銷方式需要具有表達資料項目間關係的資訊技術，而關聯規則(association rule)是一種支援共同行銷所需之資料倉儲中的極重要元件。
傳統關聯規則的挖掘可以用來找出交易資料庫中客戶潛在的消費傾向。如果得以進一步的鎖定是那些客戶在什麼時間、什麼地點具有這種消費傾向，我們可藉此制定更精確、更具獲利能力的行銷策略。然而，大部分的相關習成技術都假設挖掘出的規則在資料庫的每一個區間都是一樣有效的，然而這顯然不符合大多數的現實狀況。
本研究主要著眼於如何有效率的在不同維度、不同大小的資料庫區域中挖掘關聯規則。藉此發展出可以自動在資料庫中產生分割的機制。就此，本研究提出一個方法找出在各個分割中成立的關聯規則，此一方法具有以下幾個優點：
1. 對於找出的關聯規則，可以進一步界定此規則在資料庫的那些區域成立。
2. 對於使用者知識以及資料庫重覆掃瞄次數的要求低於先前的方法。
3. 藉由保留中間結果，此一方法可以做到增量模式的規則挖掘。
本研究舉了兩個例子來驗證所提出的方法，結果顯示本方法具有效率及可規模化方面均較以往之方法為優。
In today’s customer-oriented market, vision of “For better customer, the better service” becomes “For every customer, the appropriate service”. Companies can develop composite products to satisfy customer needs by cross-selling. In Taiwan’s financial sector, many financial holding companies have been consecutively founded recently. By pooling the resources and capital for banking, insurance, and securities, these financial holding companies would like to integration information resources from subsidiary companies for cross-selling. This new promotion method needs the information technology which can present the relationship between items, and association rule is an important element in data warehouse which supports cross-selling.
Traditional association rule can discover some customer purchase trend in a transaction database. The further exploration into targets as when, where and what kind of customers have this purchase trend that we chase, the more precise information that we can retrieve to make accurate and profitable strategies. Moreover, most related works assume that the rules are effective in database thoroughly, which obviously does not work in the majority of cases.
The aim of this paper is to discover correspondent rules from different zones in database. We develop a mechanism to produce segmentations with different granularities related to each dimension, and propose an algorithm to discover association rules in all the segmentations. The advantages of our method are:
1. The rules which only hold in several segmentations of database will be picked up by our algorithm.
2. Mining all association rules in all predefined segmentations with less user prior knowledge and redundant database scans than previous methods.
3. By keeping the intermediate results of the algorithm, we can implement an incremental mining.
We give two examples to evaluate our method, and the results show that our method is efficient and effective.
參考文獻 [1] Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Privacy preserving mining of association rules, Information System, 29 (2004) 343-364.
[2] Bernd Vindevogel, Dirk Van den Poel, Geert Wets, Why promotion strategies based on market basket analysis do not work, Expert System with Application , 28 (2005) 583-590.
[3] Bing Liu, Wynne Hsu, Yiming Ma, Mining Association Rules with Multiple Minmum Supports, ACM SIGKDD International Conference in Knowledge Discovery & Data Mining, San Diego, CA, USA, August 15-18 (1999).
[4] Brian Lent, Arun Swami, Jennifer Widom, Clustering Association Rules, 13th International Conference on Data Engineering, (1997).
[5] Chris Rygielski, Jyun-Cheng Wang, David C. Yen, Data mining techniques for customer relationship management, Technology in Society, 24 (2002) 483-502.
[6] David Hand, Heikki Mannila, Padhraic Smyth, Principles of Data Mining, (2001), London, 427-448.
[7] Guoqing Chen, Qiang Wei, Fuzzy association rules and the extended mining algorithms, Information Sciences, 147 (2002) 201-228
[8] Honghun Lu, Ling Feng, Jiawei Han, Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules, ACM Transactions on Information System, 18(4) (2000) 423-454.
[9] Jiawei Han, Guozhu Dong, Yiwen Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database, Proceedings of the 15th International Conference on Data Engineering, (1995)106-115.
[10] Jiawei Han, Wan Gong, Yiwen Yin, Mining Segment-Wise Periodic Patterns in Time-Related Databases, International Conference on Knowledge Discovery and Data Mining, New York City, NY, August (1998).
[11] Jiawei Han, Yiwen Yin, Mining frequent patterns without candidate generation, Proceedings of the ACM-SIGMOD International Conference on Management of Data, Dallas, TX, May (2000).
[12] Jiawei Han, Yongjian Fu, Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Database, AAAI’94 Workshop on knowledge Discovery in Databases, 157-168, Seattle, WA, July (1994).
[13] Jiawei Han, Yongjian Fu, Discovery of Multiple-Level Association Rules from Large Databases, Proceedings of the 21th International conference on Very Large Database, Zurich, Switzerland, (1995).
[14] Jiawei Han, Micheline Kamber, Data Mining – concepts and techniques, San Francisco, Morgan Kaufmann, (2001).
[15] Jiawei Han, Yongjian Fu, Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases, KDD Workshop, Seattle, Washington, USA, (1994).
[16] Johan de Kleer, An Assumption-based TMS, Artificial Intelligence, 28(2)(1986)127-162.
[17] Johan de Kleer, Extending the ATMS, Artificial Intelligence, 28(2) (1986) 163-196.
[18] Johan de Kleer, Problem Solving with the ATMS, Artificial Intelligence, 28(2) (1986) 197-224.
[19] Johan de Kleer, Brian C. Williams, Diagnosing Multiple Faults, Artificial Intelligence, 32(1) (1987) 97-130.
[20] Kenneth D. Forbus, QPE: Using assumption-based truth maintenance for qualitative simulation, Artificial Intelligence in Engineering, 3(4) (1988) 85-168.
[21] Pauray S.M. Tasi, Chien-Ming Chen, Mining interesting association rules from customer databases and transaction databases, Information Systems, 29 (2004) 685-696.
[22] Rakesh Agrawal, John C. Shafer, Parallel Mining of Association Rules, IEEE Transactions on Knowledge and Data Engineering, 8(6) (1996) 962-969.
[23] Rakesh Agrawal, Ramakrishnan Srikant, Fast algorithms for mining association rules in large databases, Proceedings of the International Conference on Very Large Data Bases, (1994) 487–499.
[24] Ramakrishnan Srikant, Rakesh Agrawal, Mining Generalized Association Rules, Proceedings of the 21th VLDB Conference, Zurich, Swizerland, (1995).
[25] Ramakrishnan Srikant, Rakesh Agrawal, Mining Quantitative Association Rules in Large Relational Tables, Proceedings of the ACM-SIGMOD 1996 Conference on Management of Data, Montreal, Canada, June (1996) 1-12.
[26] Roberto J, Bayaede Jr., Rakesh Agrawal, Mining the most interesting rules, Proceedings of the 5th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, August (1999) 145-154.
[27] Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur, Dynamic Itemset Counting and Implication Rules for Market Basket Data, Proceedings ACM SIGMOD international Conference on Management of Data, (1997) 255-264.
[28] Sheng Ma, Joseph L. Hellerstein, Mining Mutually Depaendent Patterns, IEEE International Conference in Data Mining, (2001).
[29] Dirk Van den Poel, Jan De Schamphelaere, Geert Wets, Direct and indirect effects of retail promotions, Expert Systems with Applications, 27(1) (2004) 53-62.
[30] Wei Wang, Jiong Yang, Richard Muntz, Tempoeal Association Rules with Numberical Attributes, Proceedings of the 17th International Conference on Data Engineering, (2001) 283-292.
[31] William J. Frawley, Gregory Piatetsky-Shapiro, Christopher J. Matheus, Knowledge discovery in database: An overview, Knowledge discovery in Database, (1991) 1-27
[32] Yen-Liang Chen, Kwei Tang, Ren-Jie Shen, Ya-Han Hu, Market basket analysis in a multiple store environment, Decision Support System, 40(2) (2005) 339-354.
[33] Yingjiu Li, Peng Ning, X. Sean Wang, Sushil Jajodia, Discovering calendar-based temporal association rules, Data & Knowledge Engineering 44 (2003) 193-218.
[34] 姜國揮, 黃惠卿, 資訊科技在共同行銷應用之研究 ~ 以銀行與保障業務為例, 1994.9.
描述 碩士
國立政治大學
資訊管理研究所
92356034
93
資料來源 http://thesis.lib.nccu.edu.tw/record/#G0923560341
資料類型 thesis
dc.contributor.advisor 姜國輝zh_TW
dc.contributor.advisor CHIANG,Johannes K.en_US
dc.contributor.author (作者) 吳家齊zh_TW
dc.contributor.author (作者) Wu,Chia-Chien_US
dc.creator (作者) 吳家齊zh_TW
dc.creator (作者) Wu,Chia-Chien_US
dc.date (日期) 2004en_US
dc.date.accessioned 18-九月-2009 14:37:32 (UTC+8)-
dc.date.available 18-九月-2009 14:37:32 (UTC+8)-
dc.date.issued (上傳時間) 18-九月-2009 14:37:32 (UTC+8)-
dc.identifier (其他 識別碼) G0923560341en_US
dc.identifier.uri (URI) https://nccur.lib.nccu.edu.tw/handle/140.119/35280-
dc.description (描述) 碩士zh_TW
dc.description (描述) 國立政治大學zh_TW
dc.description (描述) 資訊管理研究所zh_TW
dc.description (描述) 92356034zh_TW
dc.description (描述) 93zh_TW
dc.description.abstract (摘要) 在今日以客戶為導向的市場中，“給較好的客戶較好的服務”的概念已經逐漸轉變為“給每一位客戶適當的服務”。藉由跨域行銷(cross-selling)的方式，企業可以為不同的客戶提供適當的服務及商品組合。臺灣的金融業近年來在金融整合中陸續成立了多家金融控股公司，希望藉由銀行、保險與證券等領域統籌資源與資本集中，以整合旗下子公司達成跨領域的共同行銷。這種新的行銷方式需要具有表達資料項目間關係的資訊技術，而關聯規則(association rule)是一種支援共同行銷所需之資料倉儲中的極重要元件。
傳統關聯規則的挖掘可以用來找出交易資料庫中客戶潛在的消費傾向。如果得以進一步的鎖定是那些客戶在什麼時間、什麼地點具有這種消費傾向，我們可藉此制定更精確、更具獲利能力的行銷策略。然而，大部分的相關習成技術都假設挖掘出的規則在資料庫的每一個區間都是一樣有效的，然而這顯然不符合大多數的現實狀況。
本研究主要著眼於如何有效率的在不同維度、不同大小的資料庫區域中挖掘關聯規則。藉此發展出可以自動在資料庫中產生分割的機制。就此，本研究提出一個方法找出在各個分割中成立的關聯規則，此一方法具有以下幾個優點：
1. 對於找出的關聯規則，可以進一步界定此規則在資料庫的那些區域成立。
2. 對於使用者知識以及資料庫重覆掃瞄次數的要求低於先前的方法。
3. 藉由保留中間結果，此一方法可以做到增量模式的規則挖掘。
本研究舉了兩個例子來驗證所提出的方法，結果顯示本方法具有效率及可規模化方面均較以往之方法為優。		zh_TW
dc.description.abstract (摘要) In today’s customer-oriented market, vision of “For better customer, the better service” becomes “For every customer, the appropriate service”. Companies can develop composite products to satisfy customer needs by cross-selling. In Taiwan’s financial sector, many financial holding companies have been consecutively founded recently. By pooling the resources and capital for banking, insurance, and securities, these financial holding companies would like to integration information resources from subsidiary companies for cross-selling. This new promotion method needs the information technology which can present the relationship between items, and association rule is an important element in data warehouse which supports cross-selling.
Traditional association rule can discover some customer purchase trend in a transaction database. The further exploration into targets as when, where and what kind of customers have this purchase trend that we chase, the more precise information that we can retrieve to make accurate and profitable strategies. Moreover, most related works assume that the rules are effective in database thoroughly, which obviously does not work in the majority of cases.
The aim of this paper is to discover correspondent rules from different zones in database. We develop a mechanism to produce segmentations with different granularities related to each dimension, and propose an algorithm to discover association rules in all the segmentations. The advantages of our method are:
1. The rules which only hold in several segmentations of database will be picked up by our algorithm.
2. Mining all association rules in all predefined segmentations with less user prior knowledge and redundant database scans than previous methods.
3. By keeping the intermediate results of the algorithm, we can implement an incremental mining.
We give two examples to evaluate our method, and the results show that our method is efficient and effective.		en_US
dc.description.tableofcontents Table of Contents
Table of Contents 6
List of Illustrations 8
List of Tables 10
1. Introduction 11
2. Literature review 14
2.1. Association rule 14
2.1.1. Definition of association rules 15
2.1.2. Mining association rules 16
2.1.3. Entropy function & application 17
2.1.4. Mining association algorithm 18
2.2. Multi-dimension association rule 20
2.2.1. Quantitative association rules 20
2.2.2. Relationship graph 22
2.2.3. Association rule clustering system (ARCS) 24
2.3. Calendar-based temporal association rules 27
2.3.1. Calendar schema and calendar pattern 27
2.3.2. Calendar-based temporal association rules 28
2.4. Concept hierarchy 29
3. Problem definition 32
3.1. Multi-dimension transaction database 32
3.2. A dimension in MD 33
3.2.1. Dimension atom 34
3.2.2. Dimension compound 35
3.2.3. Concept hierarchy with lattice structure 36
3.3. Multi-dimension pattern 36
3.3.1. Element patterns and generalized patterns 37
3.3.2. Element segmentations 38
3.3.3. Combination segmentations 39
3.4. Multi-dimension association rules 40
3.4.1. Multi-dimension association rule w.r.t full match 40
3.4.2. Multi-dimension association rule w.r.t relaxed match 41
4. Algorithm 42
4.1. Generate all patterns and pattern table 43
4.2. Update process 45
4.3. Rules output 47
5. Experiments 48
5.1. A whole selling example 48
5.1.1. Experiment scenario 48
5.1.2. Data generation 49
5.1.3. Experiment result 51
5.2. A financial example 55
5.2.1. Experiment scenario 55
5.2.2. Data generation 56
5.2.3. Experiment result 56
6. Conclusion and future works 58
6.1. Conclusion 58
6.2. Future works 61
Appendix A. Counting strategy 62
Appendix B. Proof sketches 64
Reference 65


List of Illustrations
Figure 2.1 transaction database D 15
Figure 2.2 Initial candidate space for the circuit example 16
Figure 2.3 Association rules mining algorithm 18
Figure 2.4 Example of a customer table 21
Figure 2.5 Mapping to Boolean association rules problem 21
Figure 2.6 Part of quantitative association mining result 21
Figure 2.7 Example of transaction and customer database 22
Figure 2.8 Example of relationship graph 23
Figure 2.9 Algorithm of relationship graph 24
Figure 2.10 An example of BinArray 25
Figure 2.11 An example of marked BinArray 26
Figure 2.12 Result and output rules 26
Figure 2.13 An example of calendar schema and calendar patterns 28
Figure 2.14 Outline of algorithm in calendar-based temporal association rules. 29
Figure 2.15 An example of temporal hierarchy 30
Figure 2.16 Hierarchical and lattice structures of attributes in warehouse dimensions 31
Figure 2.17 An example of salary hierarchy 31
Figure 3.1 Multi-dimension transaction database MD 32
Figure 3.2 A transaction in MD 33
Figure 3.3 CH1 for dimension “Date” 33
Figure 3.4 Example of dimension atom in dimension “Date” 34
Figure 3.5 An example of dimension compound in dimension “Date” 35
Figure 3.6 An example of lattice structure concept hierarchy 36
Figure 3.7 Hyper-cubes diced by dimension atoms. 38
Figure 3.8 An example of element segmentation. 39
Figure 3.9 A combination segmentation is composed of element segmentations. 40
Figure 4.1 Outline of our algorithm. 42
Figure 4.2 Two given concept hierarchy. 44
Figure 4.3 belonging relationships between patterns. 44
Figure 4.4 A pattern table for concept hierarchies in Fig. 4.2. 45
Figure 4.5 Update algorithm for full match 45
Figure 4.6 An example of update for full match 46
Figure 4.7 Update algorithm for relaxed match 46
Figure 4.8 An example of update for relaxed match. 47
Figure 5.1. MD of scenario 48
Figure 5.2. Concept hierarchies of scenario 49
Figure 5.3. Scalability experiment 51
Figure 5.4. Effects of minimum support on efficient. 52
Figure 5.5. Effects of number of element patterns on efficient. 52
Figure 5.6. Effects of minsup on discrete large itemsets ratio. 53
Figure 5.7. Effects of match ratio on discrete large itemsets ratio. 54
Figure 5.8. Effects of match ratio on lost large itemsets. 54
Figure 5.9. Concept hierarchies in the example. 56
Figure 5.10. Scalability experiment 56
Figure 6.1. Example concept hierarchy I 59
Figure 6.2. Example concept hierarchy II 59
Figure 6.3 incremental mining 60

List of Tables
Table 1. Three types of data set 50
Table 2. Parameters and default values of data sets 51
Table 3. Definition of funds 55
Table 4. Symbol definitions 62		zh_TW
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dc.language.iso en_US-
dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0923560341en_US
dc.subject (關鍵詞) 資料探勘zh_TW
dc.subject (關鍵詞) 關聯規則zh_TW
dc.subject (關鍵詞) 多維度規則zh_TW
dc.subject (關鍵詞) 購物籃分析zh_TW
dc.subject (關鍵詞) data miningen_US
dc.subject (關鍵詞) association ruleen_US
dc.subject (關鍵詞) Multi-dimension ruleen_US
dc.subject (關鍵詞) Basket analysisen_US
dc.title (題名) Mining Multi-Dimension Rules in Multiple Database Segmentation-on Examples of Cross Sellingzh_TW
dc.type (資料類型) thesisen
dc.relation.reference (參考文獻) [1] Alexandre Evfimievski, Ramakrishnan Srikant, Rakesh Agrawal, Privacy preserving mining of association rules, Information System, 29 (2004) 343-364.zh_TW
dc.relation.reference (參考文獻) [2] Bernd Vindevogel, Dirk Van den Poel, Geert Wets, Why promotion strategies based on market basket analysis do not work, Expert System with Application , 28 (2005) 583-590.zh_TW
dc.relation.reference (參考文獻) [3] Bing Liu, Wynne Hsu, Yiming Ma, Mining Association Rules with Multiple Minmum Supports, ACM SIGKDD International Conference in Knowledge Discovery & Data Mining, San Diego, CA, USA, August 15-18 (1999).zh_TW
dc.relation.reference (參考文獻) [4] Brian Lent, Arun Swami, Jennifer Widom, Clustering Association Rules, 13th International Conference on Data Engineering, (1997).zh_TW
dc.relation.reference (參考文獻) [5] Chris Rygielski, Jyun-Cheng Wang, David C. Yen, Data mining techniques for customer relationship management, Technology in Society, 24 (2002) 483-502.zh_TW
dc.relation.reference (參考文獻) [6] David Hand, Heikki Mannila, Padhraic Smyth, Principles of Data Mining, (2001), London, 427-448.zh_TW
dc.relation.reference (參考文獻) [7] Guoqing Chen, Qiang Wei, Fuzzy association rules and the extended mining algorithms, Information Sciences, 147 (2002) 201-228zh_TW
dc.relation.reference (參考文獻) [8] Honghun Lu, Ling Feng, Jiawei Han, Beyond Intratransaction Association Analysis: Mining Multidimensional Intertransaction Association Rules, ACM Transactions on Information System, 18(4) (2000) 423-454.zh_TW
dc.relation.reference (參考文獻) [9] Jiawei Han, Guozhu Dong, Yiwen Yin, Efficient Mining of Partial Periodic Patterns in Time Series Database, Proceedings of the 15th International Conference on Data Engineering, (1995)106-115.zh_TW
dc.relation.reference (參考文獻) [10] Jiawei Han, Wan Gong, Yiwen Yin, Mining Segment-Wise Periodic Patterns in Time-Related Databases, International Conference on Knowledge Discovery and Data Mining, New York City, NY, August (1998).zh_TW
dc.relation.reference (參考文獻) [11] Jiawei Han, Yiwen Yin, Mining frequent patterns without candidate generation, Proceedings of the ACM-SIGMOD International Conference on Management of Data, Dallas, TX, May (2000).zh_TW
dc.relation.reference (參考文獻) [12] Jiawei Han, Yongjian Fu, Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Database, AAAI’94 Workshop on knowledge Discovery in Databases, 157-168, Seattle, WA, July (1994).zh_TW
dc.relation.reference (參考文獻) [13] Jiawei Han, Yongjian Fu, Discovery of Multiple-Level Association Rules from Large Databases, Proceedings of the 21th International conference on Very Large Database, Zurich, Switzerland, (1995).zh_TW
dc.relation.reference (參考文獻) [14] Jiawei Han, Micheline Kamber, Data Mining – concepts and techniques, San Francisco, Morgan Kaufmann, (2001).zh_TW
dc.relation.reference (參考文獻) [15] Jiawei Han, Yongjian Fu, Dynamic Generation and Refinement of Concept Hierarchies for Knowledge Discovery in Databases, KDD Workshop, Seattle, Washington, USA, (1994).zh_TW
dc.relation.reference (參考文獻) [16] Johan de Kleer, An Assumption-based TMS, Artificial Intelligence, 28(2)(1986)127-162.zh_TW
dc.relation.reference (參考文獻) [17] Johan de Kleer, Extending the ATMS, Artificial Intelligence, 28(2) (1986) 163-196.zh_TW
dc.relation.reference (參考文獻) [18] Johan de Kleer, Problem Solving with the ATMS, Artificial Intelligence, 28(2) (1986) 197-224.zh_TW
dc.relation.reference (參考文獻) [19] Johan de Kleer, Brian C. Williams, Diagnosing Multiple Faults, Artificial Intelligence, 32(1) (1987) 97-130.zh_TW
dc.relation.reference (參考文獻) [20] Kenneth D. Forbus, QPE: Using assumption-based truth maintenance for qualitative simulation, Artificial Intelligence in Engineering, 3(4) (1988) 85-168.zh_TW
dc.relation.reference (參考文獻) [21] Pauray S.M. Tasi, Chien-Ming Chen, Mining interesting association rules from customer databases and transaction databases, Information Systems, 29 (2004) 685-696.zh_TW
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