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姓名 曾宣遠(Hsuan-yuan Tseng) 查詢紙本館藏 畢業系所 企業管理學系 論文名稱 自動化辨識替代品-以某超商為例
(Identifying Substitution items from transaction data)相關論文 檔案 [Endnote RIS 格式]
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至系統瀏覽論文 ( 永不開放)
摘要(中) 在全球經濟競爭激烈的時代中,世界各國消費性商品逐漸全球化,消費者必須快速且無時無刻都接受新的資訊,並且因為物價高漲以及商品複雜化,消費者常常必須面臨到商品的選擇與替代,故對於消費者來說,可能面臨資訊超載的問題。
故企業為了能夠節省消費者蒐集產品資訊時間,提高消費者對商品的滿意度以及掌握消費者的購買習性,通常會建立商品的關聯性規則,利用關聯性規則系統,可以針對消費者的偏好,找出可能的消費習慣以及購買組合,並且提供合適的商品組合給消費者,以期待能提高消費者的購買意願,並且企業本身能夠有效地管理商品的進貨、銷貨及存貨成本。傳統的關聯替代規則的探勘是以找尋負相關為主要方法,由於傳統的關聯替代規則的探勘相當耗費資源及時間,因此相關研究皆以改善運算效能並優化系統為主要目標。
本篇為具由前瞻性之研究僅利用傳統的關聯替代規則加上經濟學替代品之公式,並且利用消費者行為模式切入來找尋特殊替代商品以及可能的商品組合,試圖運用關聯規則由交易資料庫中分析商品組合間之潛在關聯性,並透過不同的關聯性規則找出品項間差異,試圖從大量的交易資料中萃取出可能的替代品組合。而本研究探勘出可能的替代品組合以問卷方式做為實證研究結果。而結果顯示,僅利用關聯替代規則、經濟學替代品及消費者行為概念,其探勘之替代品具有一定準確性。
摘要(英) In the era of global economic highly competition ,consumables of the world has gradually become the globalization. Consumers have to accept the new information fast all the time. Because of high prices and complicated products , consumers often have to face the choice of goods and substitution items. Consumers may face the problem of information overload.
Therefore ,enterprises not only can save the time that consumers gather product but improve the satisfaction of the consumer goods and to equip consumers’’ purchasing habits. They usually establish the commodity association rules. Association rules can be used for consumer preferences and identify possible consumer habits and the purchase of the portfolio and provide the right combination of goods to the consumer to look forward to increasing consumer willingness to buy. And the enterprise itself can effectively manage commodity and inventory costs. The traditional association of alternative rules mining is to find nagtive correlation for the main method, and the traditional association exploration of alternative rules always be considered resources and time-consuming. Therefore ,the research to begin with to improve the computing performance and optimize the system as the main target.
This visionary paper is only combination of traditional association rules and the formula of the economics of alternatives ,than coupled with consumer behavior to find out the special of substitute goods and the possible combination of goods. Attempts to use the association rules from transaction database to analysis potential correlation between the
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combination of goods. And through a variety of association rules to find out the difference between items, and try to extract the portfolio of possible alternatives from the transaction data. In this paper,the mining out of the possible alternatives portfolio is be proved by questionnaire.
The study showed that only use the association rules,the concept of alternatives in economic and consumer behavior to prove the exploration of substitution items with a certain accuracy.
關鍵字(中) ★ 替代項目
★ 替代品
★ 關聯式規則關鍵字(英) ★ Substitution items
★ Substitute goods
★ Associasion論文目次 摘要................................................................................................................................. i
Abstruct .......................................................................................................................... ii
目錄............................................................................................................................... iv
圖目錄............................................................................................................................ v
表目錄........................................................................................................................... vi
第一章 緒論................................................................................................................ 1
第一節 研究背景和動機.................................................................................... 1
第二節 研究目的................................................................................................ 5
第三節 研究架構................................................................................................ 7
第二章 文獻探討........................................................................................................ 8
第一節 經濟學.................................................................................................... 8
第二節 消費者行為.......................................................................................... 13
第三節 資料探勘之關聯式規則(Association rules) ........................................ 19
第三章 研究方法...................................................................................................... 23
第四章 實證分析...................................................................................................... 30
第一節 樣本資料描述...................................................................................... 30
第二節 樣本問卷分析...................................................................................... 31
第三節 實驗結果分析...................................................................................... 32
第五章 結論與未來研究建議.................................................................................... 36
第一節 結論...................................................................................................... 36
第二節 未來研究建議...................................................................................... 37
參考文獻...................................................................................................................... 38
英文部分.............................................................................................................. 38
中文部份.............................................................................................................. 40
附表一.......................................................................................................................... 41
參考文獻 英文部分
1. Agrawal,R.and Srikant,R.(1994).“Fast Algorithms for Mining Association Rules”,Proceedings of the 20th Conference on Very Large Database, pp487-499
2. Berry A.,M.J.,and Linoff G.(1997).Data Mining Technique Techniques for Marketing,Sales & Customer Support,N.Y.:Wiley Computer Publishing.
3. Druker,Peter F.(1973),Management:Tasks,Responsibilities , Practices,New York:Harper&Row,Inc.
4. Engel,J.F.,Kollat,D.T.,and Blackwell,R.D.(1982).Consumer Behaviors, ed,Chicago:Dryden.
5. Fang K.(2000),“Integrating Swot, Qfd, and Ahp For Group Information Systems Diagnosis”,Commerce & Management Quarterly,Vol.1,No.1,pp.17~38 .
6. Frawley W.J., Piatetsky-Shapiro G.,and Matheus C.J.(1991).Knowledge discovery databases: An overview,in Knowledge Discovery in Databases(G.Piatetsky-Shapiro and W.J.Frawley,eds.),Cambridge,MA: AAAI/MIT,pp:1-27.
7. Grupe, F. H.and M. M. Owrang.(1995).“Data mining discovering new knowledge and cooperative advantage”,Information Systems Management, 12(4), 26-31.
8. Henry,A.(1995).Consumer Behavior and Marketing Action, Cincinnati,Ohio:South-Western College Publishing.
9. Han J., Kamber M.(2000).Data Mining: Concepts and Techniques.Morgan Kaufmann.
10. Marshall,A.(1890),Principlea of Economics, ed,London:Macmillan and Co., Ltd.
11. McFarlan,F.W.(1984,5-6).Information Technology Changes the Way You Compete.HarVard Business Review,98-103.
12. Parsons,G.L.(1983).Information Technology:A New Competitive Weapon. Sloan Management Review,Fall 3-14.
13. Porter,M.E.(1985).Competitive Advantage:Creating and Sustaining Superior Performance.N.Y.The Free Press.
14. Smith,A.(1973),The Wealth of Nations,New York:The Modern Libarry.
15. Usama Fayyad, Gregory Piatetsky-Shapiro, Padhraic Smyth.(1996).“From Data Mining to Knowledge Discovery in Databases,American Association for Artificial Intelligence”,AI Magazine, pp:37-54.
16. Veblen, T. B.(1899),The Theory of the Leisure Class. An Economic Study of Institutions. London: Macmillan Publishers.
17. Walters,C.G..,and Paul,W.G..(1970).Comsumer behavior,an integrated framework,Homewood,I11,R.D.Irwin Inc.
18. Wilkie,William L.(1994).Consumer behavior, ed,New York:John Wiley&Sons,Inc.
19. Teng, W.G., Hsieh M.J.,and Chen M.S.(2005) ,“A statistical framework for mining substitution rules”, Knowledge and Information Systems (2005) 7,158–178
中文部份
1. United States Census Bureau (2012),http://www.census.gov/
2. Richard Vaughn(1980),FCB方格,Foote Cone & Belding
(博達大橋廣告公司) http://wiki.mbalib.com/wiki/FCB
3. 范銘雄(2010),運用資料探勘之關聯法則探討專案執行績效與專案特性之關聯性研究,國立中央大學營建管理研究所碩士論文。
4. 李津玲(2005),綜合商品零售業教育訓練規劃之探討,國立中央大學企業管理學系碩士在職專班碩士論文。
5. 楊雲明(2007),個體經濟學(Microeconomics),智勝文化事業有限公司。
6. 林建煌(2007),消費者行為二版,華泰文化事業股份有限公司。
7. 林建煌(2009),行銷管理四版,華泰文化事業股份有限公司。
8. 經濟部商業司(2010),商業服務業年鑑(2010),中華民國經濟部
9. 吳立民(2011),國際商情雙周刊第326期,中華民國對外貿易發展協會。
10. 中華民國行政院統計處(2012),http://www.stat.gov.tw/mp.asp?mp=4
11. 行政院陸委會(2012),http://www.mac.gov.tw/welcome01/welcome01.htm
12. 中國大陸國家統計局(2012),http://www.stats.gov.cn/
13. 高盛證卷(2012),http://www.goldmansachs.com/
14. 東亞證券(2012),美國零售類股再創歷史新高,2012/3/3,取自鉅亨網電子報http://www.cnyes.com/
指導教授 許秉瑜(Ping-yu Hsu) 審核日期 2012-6-21 推文 plurk
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