探討高頻率價格樣式的知識挖掘

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姓名

張斯凱(Zue-Kai Chung) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

探討高頻率價格樣式的知識挖掘
(Knowledge Discovery of Mining Frequent Priced Pattern)

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摘要(中)

資料挖掘的技術能夠找出大量資料中隱含的資訊，其中透過關聯規則的挖掘，能夠應用在零售業與百貨業中的購物籃分析上，可幫助行銷人員更有效地規畫行銷策略，對於交叉銷售、客戶區隔、搭配購買與商品型錄的設計上都有相當大的幫助。在零售業與百貨業之中，價格一直是影響消費者購買行為的重要因素，但是過去很少有關聯規則的分析技術，將商品價格水準的變化納入關聯規則的研究中，從這個角度去觀察，會產生許多有趣的問題。傳統關聯規則只能找出經常被共同購買的商品，而在加入價格水準的因素後，更能夠幫助企業有效地了解在各種不同的價格期間內，商品組合的實際銷售表現，進一步輔助企業來評估各種較佳的商品組合訂價。
本研究提出一套完整的分析架構，改良關聯規則的挖掘方法，加入價格與數量的維度，此方法能夠考量不同價格期間的Support計算方式，得到每個樣式在不同價格組合之下應有的Support值。此分析架構能夠從大量的交易資料中，挖掘出具有價格水準的高頻率樣式，並能針對樣式在不同價格組合期間內的銷售表現，進行事實的觀察與透過分析後的建議。
最後以真實資料進行實驗，挖掘出隱含在當中的高頻率價格樣式，並依價格組合對樣式的銷售表現影響加以歸類，將價格樣式分為9個種類；另外也對原本的高頻率樣式進行了樣式價格變異的測試。最後從實驗結果中舉出幾個實例，顯示我們所提出的方法，確實能提供更多關於價格促銷的有用資訊，對於評估促銷活動的反應及整體促銷策略的制定上，都能給予很大的幫助。

摘要(英)

Association rule mining is very useful for retail business, including of cross sell, customers segment, bundle buying, and so on. Price issue is an important factor in real business environment, but there are few studies which discussed the relation between association rules and price of products. This paper presents an integral framework which can find frequent patterns under price factor. The framework also can provide the sale fact in sale data and analysis the possible suggestion according to the fact. We use real supermarket data on experiment. The actual examples of experimental results show that the framework which we present is useful for marketing or promotion.

關鍵字(中)

★ 資料挖掘
★ 關聯規則
★ 購物籃分析
★ 價格

關鍵字(英)

★ Association Rules
★ Data Mining
★ Market Basket Analysis
★ Price

論文目次

第一章　緒論 1
第二章　相關文獻探討 4
第一節訂價(Pricing) 4
第二節購物籃分析 6
第三節關聯規則 6
第四節時間關聯規則 7
第三章　定義 9
第四章　價格樣式的挖掘 14
第一節建立IP-Tree與挖掘1-Large Itemset 16
第二節產生Ck及挖掘k-Large Itemset 20
第三節每階段候選的產生 26
第四節篩選門檻的制定 30
第五節潛在分析 32
第五章　實例說明 49
第六章　實驗模擬 58
第一節實驗設計 58
第二節參數調整 63
第三節實驗結果 64
第四節實例分析 66
第七章　結論與未來研究方向 75
參考文獻 77

參考文獻

[AIS93] R.Agrawal, T.Imilienski, and A.Swami., “Mining Association Rules between Sets of Items in Large Databases,” Proc. of the ACM SIGMOD Int'l Conf. on Management of Data, Pages 207-216, 1993
[AS94] R. Agrawal, R. Srikant., “Fast Algorithms for Mining Association Rules,” Proc. of the 20th Int'l Conference on Very Large Databases, Santiago, Chile, 1994
[B95] T.J.Blischok., “Every transaction tells a story: Creating customer knowledge through market-basket analysis,” Chain Store Age Executive, Vol. 71, Pages 50-57, March 1995
[LHM01] Bing Liu, Wynne Hsu and Yiming Ma., “Discovering the Set of Fundamental Rule Changes,” Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD-2001), San Francisco, CA, 2001
[CL01] S. Wesley Changchien and Tzu-Chuen Lu., “Mining association rules procedure to support on-line recommendation by customers and products fragmentation,” Expert Systems with Applications, Vol. 20(4), Pages 326-335, 2001
[SBM98]Craig Silverstein, Sergey Brin and Rajeev Motwani., “Beyond Market Baskets: Generalizing Association Rules to Dependence Rules,” Data Mining and Knowledge Discovery, Vol. 2(1), Pages 39-68, 1998
[CTSH] Chen, Y. L., Tang, K., Shen, R. J. and Hu Y. H., “Market basket analysis in a multiple store environment,” accepted in Decision Support Systems
[SON98] A.Savasere, E.Omiecinski, and S.Navathe., “Mining for Strong Negative Associations in a Large Database of Customer Transaction,” 14th IEEE International Conference on Data Engineering,” February, 1998
[F01] Manfred Frühwirth, “A pricing model for secondary market yield based floating rate notes subject to default risk,” European Journal of Operational Research, Vol. 135, Pages 233-248, 2001
[MKSH02] Taeki Min, Sang Yong Kim, Changhoon Shin, Minhi Hahn., “Competitive nonlinear pricing with product differentiation,” International Review of Economics and Finance, Vol. 11, Pages 155-173, 2002
[JK02] Steffen Jørgensen, Peter M. Kort., “Optimal pricing and inventory policies Centralized and decentralized decision making,” European Journal of Operational Research, Vol. 138, Pages 578-600, 2002
[SL00] Nahk Hyun Sung, Jae Kyu Lee., “Knowledge assisted dynamic pricing for large-scale retailers,” Decision Support Systems, Vol. 28, Pages 347-363, 2000
[S99] David C. Smith., “Finite sample properties of tests of the Epstein-Zin asset pricing model,” Journal of Econometrics, Vol. 93, Pages 113-148, 1999
[MSM98] Kenneth C. Manning, David E. Sprott, Anthony D. Miyazaki., “Consumer Response to Quantity Surcharges: Implications for Retail Price Setters,” Journal of Retailing, Vol. 74(3), Pages 373-399, 1998
[CM94] C. Casey, C. Murphy., “Expert systems in marketing: an application for pricing new products,” Expert Systems with Applications, Vol. 7, Issue 4, Pages 545-554, 1994
[PG02]Giudici Paolo, Passerone Gianluca., “Data mining of association structures to model consumer behaviour,” Computational Statistics and Data Analysis, Vol. 38, Issue 4, Pages 533-541, 2002
[BG98] Estelle Brand and Rob Gerristen., “Association and Sequencing,” DBMS Data Mining Solutions Supplement, http://www.dbmsmag.com/9807m03.html, 1998
[W91] R. G. Walters, ”Assessing the impact of retail promotions on product substitution, complementary purchase, and inter-store sales displacement,” Journal of Marketing, Vol. 55, Page 17–28, 1991
[MAG99]P. Manchanda, A. Ansari, S. Gupta., “The shopping basket: a model for multi-category purchase incidence decisions,” Marketing Science, Vol. 18 (2), Pages 95–114, 1999
[BM01] Bose and R. K. Mahapatra., “Business data mining - a machine learning perspective,” Information and Management, Vol. 39, Pages 211-225, 2001
[PCY97] J.-S. Park, M.-S. Chen and P. S. Yu., “Using a Hash-Based Method with Transaction Trimming for Mining Association Rules,” IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 5, Pages 813-825, 1997.
[BMUT97] Sergey Brin, Rajeev Motwani, Jeffrey D. Ullman, Shalom Tsur., “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” SIGMOD, Pages 255-264, 1997
[PBTL99] N.Pasquier, Y.Bastide, R.Taouil, and L.Lakhal., “Efficient Mining of Association Rules Using Closed Itemset Lattices,” Information Systems, Vol. 24, No.1, Pages 25-46, 1999
[SSC99] Li Shen, Hong Shen, Ling, Cheng., “New algorithms for efficient mining of association rules,” Information Sciences, Vol. 118(1-4), Pages 251-268, 1999
[HPY00] J. Han, J. Pei, and Y. Yin., “Mining Frequent Patterns without Candidate Generation,” Proc. 2000 ACM-SIGMOD Int. Conf. on Management of Data, 2000
[Z00] M. J. Zaki., “Scalable algorithms for association mining,” IEEE Trans. on Knowledge and Data Engineering, Vol. 12(3), Pages 372-390, 2000.
[LPWH02] J. Liu, Y. Pan, K. Wang, and J. Han., “Mining Frequent Item Sets by Opportunistic Projection,” Proc. of 2002 Int. Conf. on Knowledge Discovery in Databases, 2002
[AS96] A Agrawal and J.C. Shafer, “Parallel Mining of Association Rules,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, 962-969, 1996
[CNFF96] D.W. Cheung, V.T. Ng, A.W. Fu, and Y. Fu., “Efficient Mining of Association Rules in Distributed Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 8, No. 6, Pages 911-922, 1996
[T96] H. Toivonen, “Sampling Large Databases for Association Rules,” The 22-th International Conference on Very Large Databases (VLDB'96), Pages 134-145, 1996
[GC99] Sanjay Goil, Alok Choudhary, “A parallel scalable infrastructure for OLAP and data mining,” International Symposium Proceedings, Pages 178 -186, 1999
[PH00] V. Pudi and J.R. Haritsa, “Quantifying the Utility of the Past in Mining Large Databases,” Information Systems, Vol. 25, No. 5, Pages 323-343, 2000
[KH95] K. Koperski and J. Han, “Discovery of Spatial Association Rules in Geographic Information Databases,” Proc. 4th Int'l Symp. on Large Spatial Databases (SSD95), Pages 47-66, 1995
[CFK00] E. Clementini, P.D. Felice, and K. Koperski, “Mining Multiple-level Spatial Association Rules for Objects with a Broad Boundary,” Data and Knowledge Engineering, Vol. 34, No. 3, Pages 251-270, 2000
[ZHLH98] Osmar R. Zaïane, Jiawei Han, Ze-Nian Li, Jean Hou, “Mining Multimedia Data,” Proc. CASCON'98: Meeting of Minds, 1998
[LAS97] B. Lent, R. Agrawal and R. Srikant, “Discovering Trends in Text Databases,” Proc. of the 3rd Int'l Conference on Knowledge Discovery in Databases and Data Mining, 1997
[AS95] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proceedings of the 7th International Conference on Data Engineering, Pages 3-14, 1995
[Z98] M.J. Zaki, “Efficient Enumeration of Frequent Sequences,” 7th International Conference on Information and Knowledge Management, Pages 68-75, 1998.
[JA99] R. J. Bayardo Jr. and R. Agrawal, “Mining the Most Interesting Rules,” In Proc. of the 5th ACM SIGKDD Int'l Conf. on Knowledge Discovery and Data Mining, 1999
[F99] A. A. Freitas, “On Rule Interestingness Measures,” Knowledge-Based Systems, Vol. 12, No. 5, Pages 309-315, 1999
[HF99] J. Han and Y. Fu, “Mining Multiple-Level Association Rules in Large Databases,” IEEE Transactions on Knowledge and Data Engineering, Vol. 11, No. 5, Pages 798-805, 1999
[RS98] R. Rastogi and K. Shim, “Mining Optimized Association Rules with Categorical and Numeric Attributes,” The 14th International Conference on IEEE Data Engineering, 1998
[KFW98] C.M. Kuok, A.W. Fu, M.H. Wong, “Mining Fuzzy Association Rules in Databases,” SIGMOD Record, Vol. 27, No. 1, Pages 41-46, 1998
[LFH00] H. Lu, L. Feng, and J. Han. “Beyond Intra-Transaction Association Analysis: Mining Multi-Dimensional Inter-Transaction Association Rules,” ACM Transactions on Information Systems, Vol. 18, No. 4, Pages 423-454, 2000
[CL01] S. Wesley Changchien and Tzu-Chuen Lu. “Mining association rules procedure to support on-line recommendation by customers and products fragmentation,” Expert Systems with Applications, Vol. 20(4), Pages 325-335, 2001
[ORS98] B.Ozden, S. Ramaswamy, A. Silberschatz, “Cyclic association rules,” Proceedings of the 14th International Conference on Data Engineering, Pages 412–421, 1998
[HDY99] J.Han, G. Dong, and Y.Yin, “Efficient Mining of Partial Periodic Patterns in Time Series Database,” Proceedings of the 15th International Conference on Data Engineering, Pages 106-115, 1999
[RMS98] S. Ramaswamy, S. Mahajan, A. Silberschatz, “On the discovery of interesting patterns in association rules,” Proceedings of the 1998 International Conference on Very Large Data Bases, Pages 368–379, 1998
[AR00] Juan M.Ale, Gustavo H. Rossi, “An Approach to Discovering Temporal Association Rules,” Proceedings of the 2000 ACM symposium on Applied computing 2000, Pages 294-300, 2000
[LCL03] Chang-Hung Lee, Ming-Syan Chen, Cheng-Ru Lin, “Progressive Partition Miner: An Efficient Algorithm for Mining General Temporal Association Rules,” IEEE Transactions on knowledge and data engineering,” Vol.15, No.4, 2003
[CCH02] Cheng Yue Chang, Ming Syan Chen and Chang Hung Lee, “Mining general temporal association rules for items with different exhibition periods,” Proceedings. 2002 IEEE International Conference on Data

指導教授

陳彥良(Yen-Liang Chen)

審核日期

2004-6-16

推文