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研究生： 許銘家
Ming-Jia Hsu
論文名稱： 自動化辨識替代品-以某超商為例
Identifying Substitution items from transaction data
指導教授： 許秉瑜
Ping-Yu Hsu
口試委員:
學位類別： 博士
Doctor
系所名稱： 管理學院 - 企業管理學系
Department of Business Administration
論文出版年： 2018
畢業學年度： 106
語文別： 中文
論文頁數： 46
中文關鍵詞： 替代項目 、替代品 、關聯式規則 、負相關
外文關鍵詞： Substitution items, Substitute goods, Association rules, Negative correlation
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    • 摘要
    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
    以找尋負向關聯法則為主要方法，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。
    本研究為具由前瞻性之自動化辨識商品替代關係的研究，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不
    同時節下，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。經由本研究探勘出的
    可能替代品組合，將以相關係數中的負相關做為實證研究結果。而結果顯示，
    利用關聯替代規則、經濟學替代品及消費者行為概念，所探勘之替代品有近七
    成合於負相關之替代規則，顯示本研究具有一定準確性。

    Abstruct
    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 negative 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, then 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 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 the negative correlation in correlation coefficient.
    The study showed that utilize the concept of association rules together with the alternatives in economic and the consumer behavior, nearly 70% of the substitutes were subject to the negative correlation alternative rules; hence, it shows that this study has certain accuracy.

    目錄 摘要 .................................................................................................................................................................i Abstruct ....................................................................................................................................................... ii 目錄 .............................................................................................................................................................. iii 圖目錄 ......................................................................................................................................................... iv 表目錄 .......................................................................................................................................................... v 第一章 第二章 2.1 2.2 2.3 第三章 3.1 3.2 3.3 3.4 3.5 第四章 4.1 4.2 4.3 第五章 5.1 5.2 緒論 ............................................................................................................................................1 文獻探討 ..................................................................................................................................5 需求法則(law of demand)...................................................................................................5 正負向關聯性法則方法論.................................................................................................6 自動化辨識商品替代法則..............................................................................................10 研究方法 ............................................................................................................................... 11 Frequent Pattern 高頻項目集.......................................................................................12 Collating of data...................................................................................................................15 候選替代品............................................................................................................................16 替代品之判別.......................................................................................................................19 相關係數驗證.......................................................................................................................22 實證分析 ............................................................................................................................... 24 樣本資料描述.......................................................................................................................24 探勘之替代品.......................................................................................................................25 負相關驗證結果..................................................................................................................25 結論與未來研究建議 ...................................................................................................... 29 結論...........................................................................................................................................29 未來研究建議.......................................................................................................................30 參考文獻 ................................................................................................................................................... 31 英文部分 ......................................................................................................................................... 31 中文部份 ......................................................................................................................................... 33 附件 ............................................................................................................................................................. 34 iii 圖 1-1 圖 2-1 圖 3-1 圖 3-2 圖 3-3 圖 3-4 圖 3-5 圖 3-6 圖 3-7 圖 4-1 圖 4-2 圖 4-3 圖 4-4 圖 4-5 圖 4-6 圖目錄 研究架構 .....................................................................................................................................4 補項品項的負向關聯法則 ..................................................................................................8 研究方法的五個階段 ......................................................................................................... 12 Frequent Itemset ................................................................................................................... 14 Collating of data ................................................................................................................... 15 Difference ................................................................................................................................. 17 Candidate of Substitutes Item......................................................................................... 18 售價(P)取平均值，銷售量(Q)取總和 ......................................................................... 21 品項替代週期 ........................................................................................................................ 21 替代品在各分群中佔比 .................................................................................................... 25 相關係數 r 的正負相關 .................................................................................................... 26 Frequent Pattern 長度為 2，替代品運用負相關驗證比率.............................26 Frequent Pattern 長度為 3，替代品運用負相關驗證比率.............................27 Frequent Pattern 長度為 4，替代品運用負相關驗證比率.............................27 所有替代品運用負相關驗證比率 ................................................................................ 28 iv 表目錄 表 1-1 美國年度存貨額(除製造商分公司)，2010-2017..........................................................2 表3-1 交易資料之部份高頻項目集............................................................................................14 表3-2 品項4的群組之候選替代品............................................................................................19 表3-3 品項3的群組之候選替代品............................................................................................19

    參考文獻
    1. Agrawal,R.and Srikant,R.(1994).「Fast Algorithms for Mining Association Rules」,Proceedings of the 20th Conference on Very Large Database, pp487- 499,Santiago,Chile.
    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,4§•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, 8§• ed,London:Macmillan and Co., Ltd.
    31
    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,3¶$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”,KnowledgeandInformationSystems (2005)7,158– 178
    32

    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/

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