臺灣博碩士論文加值系統

面對現今產業競爭愈加劇烈的狀況，各企業除了在成本、品質、產品上積極改善之外，也引進了以顧客為主的思維來改善產品與服務。以顧客為出發點，可以更瞭解真正的需求為何，讓顧客的滿意度提高。隨著資訊科技的發展，讓這樣的想法得以具體化。於是，顧客關係管理系統興起，可以有效率地得知顧客的需求；甚至從購買的行為找出特殊的關聯、預測可能的行為模式，進而早一步制訂合適的策略，來提升顧客滿意度。
顧客在購買產品或服務之後，可能會有抱怨或建議的狀況發生。針對這些抱怨問題，企業必須要妥善處理，才能提昇顧客滿意度及忠誠度。顧客的抱怨文件中，隱含許多寶貴的資訊，可以作為企業的知識庫及決策的參考，並回饋給顧客最佳的解決方法。因此，從非結構化的客訴文件中去找出可能的關聯是本研究所關心的，並試圖將這些資訊予以外顯化、具體化，以便可以實際運用於顧客關係管理的改善。
本研究由文件管理的角度出發，配合文件探勘的相關技術，從顧客抱怨文件中分析出各文件的關鍵詞彙，並以此為基礎來探勘其關聯法則。由於文件中詞彙出現的頻率並不足以代表其關鍵概念，因此本研究以相關背景知識建立概念階層架構，用來輔助探勘的進行。各關聯的相關程度則以各詞彙出現的機率來加以判斷，以區別出各關聯法則的正確度。

摘要I
誌謝II
目次III
表目錄V
圖目錄VI
第1章 緒論1
1.1 研究動機1
1.2 研究目的2
1.3 研究流程3
第2章 文獻探討4
2.1 顧客抱怨4
2.1.1 客訴（顧客抱怨）的定義4
2.1.2 顧客抱怨的原因與行為6
2.1.3 顧客抱怨的處理10
2.2 文件管理13
2.2.1 文件與流程關係13
2.2.2 電子文件管理環境14
2.2.3 文件管理系統18
2.3 案例推論20
2.3.1 案例推論之理論架構20
2.3.2 專家知識22
2.3.3 案例推論之知識擷取23
2.3.4 案例推論之優點26
2.3.5 案例推論循環26
2.4 文件探勘28
2.4.1 布林擷取29
2.4.2 字元相關30
2.4.3 文件表達31
2.4.4 向量空間模型32
2.4.5 文件探勘系統33
2.5 關聯法則探勘35
2.5.1 關聯法則35
2.5.2 階層式概念圖37
第3章 研究方法39
3.1 研究架構39
3.2 關鍵資訊擷取41
3.2.1 文件詞彙擷取41
3.2.2 關鍵資訊擷取42
3.2.3 文件頻率43
3.3 文件分類46
3.3.1 向量表示法46
3.3.2 文件相關度比對47
3.4 關聯法則探勘50
3.4.1 Apriori演算法50
3.4.2 概念階層53
3.4.3 知網分類架構54
第4章 實作步驟與方法56
4.1 系統架構56
4.2 關鍵詞彙擷取58
4.2.1 資料來源58
4.2.2 文件斷詞59
4.2.3 關鍵詞彙篩選60
4.2.4 詞彙處理64
4.3 相似度比較分析64
4.3.1 相似度計算方式64
4.3.2 相似度分佈情形65
4.4 關聯法則探勘68
4.4.1 背景知識取得68
4.4.2 概念階層建立68
4.4.3 探勘結果分析70
第5章 結論與建議72
參考文獻74

一、中文部分
1.中央研究院詞庫小組, “中文分詞系統”, http://godel.iis.sinica.edu.tw/CKIP/r_content.htm
2.中村卯一郎著(1992)，謝文龍譯，抱怨處理讀本─化「抱怨」為企業「利潤」的法則，台北：遠流出版公司，民81，初版。
3.王美音、揚子江合譯，P. Drucker 著，創新與創業(Innocation & Entrepreneurship, 1987)，台北：卓越文化出版，民76，初版。
4.佐藤知恭著(1988)，黃己城譯，顧客抱怨處理實務，台北：臺華工商圖書公司，民87，初版。
5.林大容譯，L. Edvinsson & M. S. Malone著，智慧資本(Intellgence Capital, 1999)，台北：麥田出版，民88，初版。
6.邱振儒譯，Wayland, R. E. and Cole, P. M.著，客戶關係管理(Customer connections — new strategies for growth, 1997)，台北：時報文化出版，民88，初版。
7.陳耀茂著(民86)，服務品質管理手冊，台北：遠流出版公司，初版。
8.湯姆‧麥克阿瑟(民81）。朗文多功能分類辭典(英英.英漢雙解)。培生教育。
9.董振東,董強, “知網”, http://how-net.com.
二、英文部分
1.Aamodt, A. & Plaza, E. (1994), “Case-Based Reasoning: Foundational Issues, Methodological Variations and System Approaches,” AI-Communications.
2.Agrawal, R. & Srikant, R. (1994), “Fast Algorithms for Mining Association Rules,” Proceedings of the 20th International Conference on Very Large Databases, Santiago. 487-499.
3.Agrawal, R., Imielinski , T., & Swami, A. (1993), “Mining Association Rules between Sets of Items in Large Databases,” Proceedings of the 1993 ACM SIGMOD Conference, 207-216.
4.Agrawal, R., T. Imielinski, & Swami, A. (1993), “Mining Association Rules between Sets of Items in Large Databases,” Proceedings of the 1993 ACM SIGMOD Conference, 207-216.
5.Aumann et al., Y. (1999), “Circle Graphs:New Visualization Tools for Text-Mining,” Proceedings of Third European Conference on KDD(PKDD-99), 165-173.
6.Bae H. & Kim, Yeongho (2002), “A document-process association model for workflow management,” Computers in Industry, 47, 139-154.
7.Balasubramanian, V. & Bashian, A. (1998), “Document Management and Web Technologies: Alice Marries the Mad Hatter,” Communications of the ACM, 41(7), 107-115.
8.Bitner, M. J., Booms, B.G., & Tetreault, M. S. (1990), “The Service Encounter : Diagnosing Favorable and Unfavorable Incidents,” Journal of Marketing , 54 , 71-84.
9.Bookstein, A, Klein, S. T., & Raita, T. (1995), “Detecting content bearing words by serial clustering,” SIGIR Forum (ACM Special Interest Group on Information Retrieval), 319-327.
10.Braa, K. & Sandahl, T. I. (1998), “Approaches to standardization of documents.” In T. Wakayama, S. Kannapan, C. M. Khoong, S. Navanthe, & J. Yates, Information and Process Integration in Enterprises: Rethinking Documents (pp. 125-142). Norwell (MA): Kluwer Academic Publishers.
11.Burnett, J. J., Amason, R.D., & Hunt, S.D. (1981), “Feminism:Implications For Department Store Strategy and Salesclerk Behavior,” Journal of Retailing , 57(4), 71-85.
12.Chang, H. —C., Dong, L., Liu, F.X. & Lu, W.F. (2000), “Indexing and retrieval in machining process planning using case-based reasoning,” Artificial Intelligence in Engineering, 14, 1-13.
13.Chen, K. J. & Kiu, S. H. (1992), “Word identification for Mandarin Chinese sentences,” Fifth International Conference on Computational Linguistics, 101-107.
14.Cohen, J.D. (1995), “Highlights: language- and domain-independent automatic indexing terms for abstracting,” Journal of the American Society for Information Science, 46(3), 162-174.
15.Day, R.L. & Landon, E. L., (1977), “Toward a Theoty of Consumer Complaining Behaviour,” in Consumer & Industrial Buying Behaviour, A., Woodside, J. Sheth & P. Bennett, eds. Amsterdam, The Netherl&s :North-Holl& Publishing Company Press.
16.Day, R.L. (1980), “Research Perspectives on Consumer Complaining Behaviour,” in Theoretical Developments in Marketing, Charles Lamb & Patyick Dunne, eds. Chicago, FL : American Marketing Association, 211-215.
17.Day, R.L., Schaetzle, G. T. & Staubach, F. (1981), “The Hidden Agenda of Consumer Complaining,” Journal of Retailing , 57, 86-106.
18.Dörre, J., Gerstl, P. & Seiffert, R. (1999), “Text Mining: Finding Nuggets in Mountains of Textual Data”, Proceedings of the 5’s ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 398-401.
19.Dourish, P. , Edwards, K., Lamarca, A., Lamping, J., Perersen, K., Salisbury, M., Terry, D. & Thornton, J. (2000), “Extending Document Management Systems with User-Specific Active Properties,” ACM Transactions on Information System, 18(2), 140-170.
20.Ellis, C. A. (1979), “Information Control Nets: A mathematical model of office information flow.” Proceedings of the Conference on Simulation, Measurement and Modeling of Computer Systems (special issue). ACM SIGMETRICS Performance Evaluation Review, 8(3), 225-238.
21.Fan, C. K. & Tsai, W. H. (1998), “Automatic Word Identification in Chinese Sentences by the Relaxation Technique,” Computer Proceeding of Chinese and Oriental Languages, 33-56.
22.Feldman, R. & Dagan, I. (1995), “Knowledge Discovery in Textual Database(KDT),” Proceedings of the first ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 112-117.
23.Feldman, R. & Hirsh, H. (1996), “Mining Association in Text in the Presence of Background Knowledge,” Proceedings of 2’nd international Conference on Knowledge Discovery and Data Mining, 343-346.
24.Feldman, R., Klosgen, W., & Zilberstein, A. (1997a), “Visualization Techniques to explore Data Mining Result s for Document Collections,” Proceedings of the Third International Conference on Knowledge Discovery & Data Mining, 16-23.
25.Feldman et al., R. (1997b), “Pattern Based Browsing in Document Collections,” Proceedings of First European Symposium on Principles of Data Mining and Knowledge Discovery, 112-122.
26.Feldman, R. & Hirsh, H. (1997c), “Exploiting Background Information in Knowledge Discovery from Text,” Journal of Information System, 83-97.
27.Fornell, C. & Wernerfelt, B. (1987), “Defensive Marketing Strategy by Consumer Complaint Management : A Theoretical Analysis,” Journal of Marketing Research, 24, 337-346.
28.Freeman, E. & Fertig S. (1995), “Lifestreams: Organizing your electronic life,” In Proceeding of the AAAI Fall Symposium on AI Applications in Knowledge Naviation and Retrieval, AIII Press, Menlo Park, CA..
29.Gifford, D., Jouvelot, P. , Sheldon, M., & O’Toole, J. (1991), “Semantic file systems,” In Proceedings of the Thirteenth ACM Symposium on Operating System Principles, ACM Press, New York.
30.Hamilton et al. (1997), “Parallel knowledge discovery using domain generalization graphs.” Department of Computer Science University of Regina.
31.Hamilton, H. J., Hilderman, R. J., & Cercone, N. (1996), “Attribute-oriented induction using domain generalization graphs,” In Proceedings of the Eighth IEEE International Conference on Tools with Artificial Intelligence (ICTAI ’96), 246-253.
32.Han, J. (1994), “Towards efficient induction mechanisms in databases,” Theoretical Computer Science, 361-385.
33.Heaps, H. S. (1978), “Information retrieval, computational and theoretical aspects,” Academic Press.
34.Heck, M. (2000), “Document management fuels e-business,” InfoWorld, 22(32), 59-60.
35.Hoffman, K.D., Kelley, S.W. & Rotalsky, H.M. (1995), “Tracking Service Failures and Employee Recovery Efforts,” Journal of Services Marketing , 9(2), 49-61.
36.Jacoby, J. & Jaccard, J. J. (1981), “The Sources, Meaning & Validity of Consumer Complaining Behaviour : A Psychological Review,” Journal of Retailing, 57, 4-24.
37.Janet Kolodner (1993), “Case-Based Reasoning,” Morgan Kaufmann Publisher, 355.
38.Kelley, S.W., Hoffman, K.D. & Davis, M.A. (1993), “A Typology of Retail Failures and Recoveries,“ Journal of Retailing , 69(4), 429-452.
39.Klemettinen, M., Mannila, H., & Verkamo, A. I. (1999), “Association Rule Selection in a Data Mining Environment,” PKDD-99, pp.372-377.
40.Kolter, P. (1997), “Marketing Management : Analysis, Planning,Implementation, & Control,” 9th ed., Upper Saddle River, N.J. :Prentice-Hall Press.
41.Lee, K-S., Park, Y-C. & Choi, K-S. (2001), “Re-ranking model based on document clusters,” Information Processing and Management, 37, 1-14.
42.Luhn, H. P. (1958), “The Automatic Creation of Literature Abstracts,” IBM Journal of Research and Development, 2 (2), 159-165 and 317.
43.Lyytikäinen, V. (1998), “Rakenteisuuden hyödyntäminen elektronisissa dokumenteissa.” SGML-pohjaisen dokumentaation tutkimus ja käyttö Suomessa 1997, Teknologiakatsaus 57/98, Tekes.
44.Meadow, Charles T. (1992), “Text Information Retrieval Systems,” Academic Press.
45.Nie, J., Briscbois, M. & Ren, X. (1996), “On Chinese Text Retrieval,” Conference Proceedings of SIGIR, 225-233.
46.Oakes, Michael, P., & Taylor, Malcolm J. (1998), “Title Automated assistance in the formulation of search statements for bibliographic databases,” Information Processing and Management, 34(6), 645-668.
47.Reichheld, F. F. (1996), “Learning from Consumer Defections,” Harvard Business Review, 74(1), 56-69.
48.Renoux, Y. (1973), “Consumer Dissatisfaction and Public Policy,” in Public Policy and Marketing,F.C.Allvine,ed., Chicago : American Marketing Association, 53-65.
49.Ross, Sara., Fang, Liping. & Hipel, Keith W. (2002), “A case-based reasoning system for conflict resolution: design and implementation,” Engineering Applications of Artificial Intelligence, 15, 369-383.
50.Salminen, A., Lyytikäinen, V., & Tiitinen, P. (2000), “Putting documents into their work context in document analysis.” Information Processing and Management, 36, 623-641.
51.Salton, Gerard. & Buckley, Chris. (1996), “Term Weighting Approaches in Automatic Text Retrieval,” Technical Report TR87-881, Department of Computer Science, Cornell University, 1987. Information Processing and Management, 32(4), 431-443.
52.Salton, Gerard. (1989), “Automatic Text Processing,” Addison-Wesley Publishing Company.
53.Singh et al., L. (1999), “An Algorithm for Constrained Association Rule Mining in Semi-structured Data,” PAKDD-99, 148-158.
54.Singh, J. & Widing II, R. E. (1991), “What Occurs Once Consumers Complain? A Theoretical Model for Underst&ing Satisfaction / Dissatisfaction Outcomes of Complaint Response,” European Journal of Marketing , 25(5), 30-46.
55.Singh, J. (1988), “Consumer Complaint Intentions & Behaviour : Definitional & Taxonomical Issues,” Journal of Marketing , 52, 93-107.
56.Singh, L., Scheuermann, P., & Chen, B. (1997), “Generating Association Rules from Semi-Structured Documents Using an Extended Concept Hierarchy,” ACM IKM, 193-200.
57.Singhal, A. & Salton, G. (1993), “Automatic Text Browsing Using Vector Space Model.” Technical Report, Department of Computer Science, Cornell University.
58.Smith, J. M. (1990), “Introduction to CALS: The Strategy and the Standards.” Twickenham: Technology Appraisals Ltd.
59.Spark Jones, K. (1972), “A static interpretation of term specificity and its application in retrieval,” Journal of Document, 28(1), 11-20.
60.Sperberg-McQueen, C. M. & Burnard, L. (1994), “Guidelines for electronic text encoding and interchange.” Association for Computers and the Jumanities, ACH. Is also made available by the Electronic Text Center at the University of Virginia at URL: Http://etext.virginia.edu/ TEI.html.
61.Sprague, R. H. (1995), “Electronic document management: challenges and opportunities for information systems managers.” MIS Quarterly, 19(1), 29-49.
62.Sproat, R. & Shih C. (1990), “A Statistical Method for Finding Word Boundaries in Chinese Text,” Computer Processing of Chinese and Oriental Languages, 336-351.
63.The Davenport Group (1998), “The maintainers of the DocBook DTD.” URL: http://www.oreilly.com/davenport/.
64.van Rijsbergen, C. J. (1979), “Information retrieval,” Butterworths.
65.Westbrook, R. A. (1981), “Sources of Consumer Satisfaction with Retail Outlets,” Journal of Retailing , 57(4), 68-85.