博碩士論文 etd-0726100-135739 詳細資訊


[回到前頁查詢結果 | 重新搜尋]

姓名 孫冠華(Kuan-Hua Sun) 電子郵件信箱 E-mail 資料不公開
畢業系所 資訊管理學系研究所(Information Management)
畢業學位 碩士(Master) 畢業時期 88學年第2學期
論文名稱(中) 圖書館新書推薦之個人化服務方法
論文名稱(英) A Data Mining Methodology for Library New Book Recommendation
檔案
  • 論文_孫冠華2.pdf
  • 本電子全文僅授權使用者為學術研究之目的,進行個人非營利性質之檢索、閱讀、列印。
    請遵守中華民國著作權法之相關規定,切勿任意重製、散佈、改作、轉貼、播送,以免觸法。
    論文使用權限

    電子論文:校內立即公開,校外一年後公開

    論文語文/頁數 中文/71
    統計 本論文已被瀏覽 5353 次,被下載 3250 次
    摘要(中) 顧客化的資訊提供對於資訊提供者愈來愈重要,傳統專題選粹服務(SDI)因為需要讀者主動的提供資訊,而使得服務的對象小,而且需要讀者參興。使用資料採礦技術能從讀者的借閱記錄中找出讀者的行為,提供顧客化的資訊功能。本研究以中山大學圖書館為資料來源,經由一步步的實作資料採礦的過程,期望對於資料採礦技術應用在圖書館的新書資訊提供上,有具體可行顧客化的資訊服務。
    本研究使用讀者概念階層及書目階層,並給定所需要的參數門檻值,找到滿足條件之某類讀者會借閱某類書的規則。為此本研究提出四種演算法,SBSP,SBMP,LatSBMP,MBMP適用於不同情況,並分析其執行效率,對於採礦的過程,本研究採用一般資料採礦的模式,說明實作的每一個步驟。
    摘要(英) Customized information service is very important for service provider nowadays. Traditional selective dissemination, as widely discussed in library community requires users’ involvement and only serves a limited amount of users. In this thesis, we propose to employ data mining techniques to discover knowledge in circulation databases so as to provide customized service in library new book recommendation. Our research’s data source is from National Sun Yat-Sen University’s library. We follow a standard data mining procedure and report our experience in this thesis.
    Our research uses patron concept hierarchy and book hierarchy with given support threshold and confidence threshold to derived association rules with patron types being antecedent and book types being subsequent. Four algorithms, namely SBSP, SBMP, LatSBMP, MBMP are proposed to facilitate patron and book hierarchy search. Their complexities are compared analytically.
    關鍵字(中)
  • 資料挖掘
  • 專題選粹服務
  • 新書推薦
  • 數位圖書館
  • 關鍵字(英)
  • New Book Recommendation
  • Digital Library
  • Selective Dissemination of Information
  • Data Mining
  • 論文目次 圖表目錄 2
    第一章 緒論 4
    第二章 研究目的與動機 5
    第三章 文獻探討 7
        3.1知識發現與資料採礦方法論(Data Mining Methodology) 7
        3.2資料採礦技術之相關規則分析(Association rule) 8
        3.3圖書館管理 11
    第四章 圖書館新書推薦資料採礦進行方式 13
        4.1知識發掘與資料採礦過程方法的建構 13
         人力資源的指派(Human resource identification). 13
         問題的界定(Problem specification). 14
         資料的評估(Data prospecting) 15
         相關領域知識(Domain knowledge elicitation). 16
         確定使用的方法論(Methodology identification) 17
         資料的前置處理(Data preprocessing) 26
         找出有用的Pattern(Pattern Discovered) 27
         導出知識的後序處理(Knowledge post-processing) 34
        4.2演算法探討 35
         規則的型態分析 53
         門檻值的訂定 54
        4.3發展實作 56
         系統建置 57
    第五章 討論 58
    第六章 結論 60
    參考文獻 61
    參考文獻 [Agga98a] C. C. Aggarwal, Z. Sun, and P. S.Yu, “Online algorithm for finding Profile Association Rules,“Proceedings of the 1998 ACM 7th international conference on Information and knowledge management, pages 86-95, 1998.
    [Agga98b] C. C. Aggarwal, P. S.Yu, “Online Generation of Association Rules,” Proceedings of the Fourteenth International Conference on Data Engineering, pages 402-411, Orlando, Florida, February 1998.
    [Agra93] R. Agrawal, T. Imielinski, and A. Swami, “Mining association rules between sets of items in large databases,” Proceedings of the ACM SIGMOD Conference on Management of Data, pages 207-216, May 1993.
    [Agra94] R. Agrawal, R. Srikant, “Fast Algorithm for Mining Association Rules,” Proceedings of the VLDB conference, pages 478-499, September 1994.
    [Anan98] S.S.Anand, A.R. Patrick, J.G Hughes, and D.A.Bell, “A Data Minig Methodology for Cross Sales,” Knowledge-Based Systems Vol.10, pages 449-461, 1998.
    [Brin97] S. Brin, R.Motwani, J. D. Ullman, and S. Tsur, “Dynamic Itemset Counting and Implication Rules for Market Basket Data,” Proceedings of the ACM SIGMOD International Conference on Management of Data, pages 255-264, New York, May 1997.
    [Fayy96] U. Fayyad, G. P. Shapiro, P. Smyth, and R. Uthurusamy, ”Advances in Knowledge Discovery and Data Mining,” AAAI/MIT Press, Califonia, 1996.
    [Han92] J. Han, Y. Cai, and N. Cercone,“Knowledge Discovery in Database: An Attribute-Oriented Approach,” Proceedings of the 18th VLDB Conference, pages 547-559, Vancouver, British Columnbia, Canada 1992.
    [Hidb98] C. Hidber, “Online Association Rule Mining,” TR-98-099, International Computer Science Institute, Berkeley, C.A, September 1998.
    [Lib88] Library of Congress. Library of Congress: Subject Headings, 11th edition Washington D.C, 1988.
    [Park95] J. S. Park, M.-S. Chen, and P. S. Yu, “An Effective Hash Based Algorithm for Mining Association Rules,” Proceedings of ACM SIGMOD, pages 175-186, May 1995.
    [Pete96] T. Peters, “Using Transaction Log Analysis for Library Management Information,” Library Administration and Management, Vol 10, pages 20-25, winter1996.
    [PuHT99] Hsiao-Tieh Pu, Sung-Chien Lin, “Exploration of practical approaches to personalized library and networked information service,” The 11th International Conference on New Information Technology, pages 333-343, Taipei, Taiwan, August, 1999.
    [Srik96] R. Srikant, R Agrawal, “Mining Quantitative Association Rule in Large Relational Tables,” Proceedings of the 1996 ACM SIGMOD Conference on Management of Data, pages 1-12, Montreal, Canada, June 1996.
    [林巧敏] 簡介圖書資料處理的方法─編目與分類,工圖館訊第四期(http://www.lib.ntu.edu.tw/pub/ek/ek04/ek04_1.html)
    [賴永祥] 賴永祥,中國圖書分類法,增訂7版,三民書局, 1989
    口試委員
  • 魏志平 - 主任委員
  • 簡立峰 - 委員
  • 黃三益 - 指導教授
  • 口試日期 2000-07-13 繳交日期 2000-07-26

    [回到前頁查詢結果 | 重新搜尋]


    如有任何問題請與論文審查小組聯繫