Title page for 86325036


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Student Number 86325036
Author Kwen-San Wang(王坤山)
Author's Email Address No Public.
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Department Computer Science and Information Engineering
Year 1998
Semester 2
Degree Master
Type of Document Master's Thesis
Language zh-TW.Big5 Chinese
Title Web log mining: discovering user access patterns
Date of Defense
Page Count 51
Keyword
  • personal web site
  • user access patterns
  • web log mining
  • Abstract The World Wide Web is becoming a key medium for information dissemination, which support different information for visitors to navigate. To build personal web site, we retrieve user preference from user access patterns; unlike previously research , we focus on multi-dimension patterns. Every page have multiply topic , different visitor is attracted by different topic , user access patterns should show why visitor access those pages instead of what they access them . We derive new approach for multi-dimension access patterns , and prove its usefulness and feasibility in our experiments. We also study how to maintain reliability of access pattern when transaction data continual update. Visitors may change their interest , the pattern discovered from database only reflect the current state of the database. To make the patterns stable and reliable, a large volume of data should be collected over a substantial period of time . We store the result of previous database to cache , for improving the speed of hit , we build index for cache. In our experiments , it will have better performance when use index.
    Table of Content CHAPTER 1
    緒論1
    1.1背景與動機:1
    1.2問題分析3
    1.3相關研究7
    1.3.1 網站日誌探勘(Web log mining)7
    1.3.2 維持pattern的正確-incremental update9
    1.4 我們的方法9
    1.5論文架構11
    CHAPTER 2 多維的使用者讀取模式12
    2.1 多維使用者讀取模式的定義12
    2.2 多維使用者讀取模式的演算法14
    2.2.1 範例14
    2.2.2 Aprior的演算法及需要修改之處15
    2.3 LITEMSET PHASE17
    2.3.1Algorithm1 (MP1)18
    2.3.2 Algorithm2 (MP2)19
    2.4 模擬多維使用者讀取模式的尋找過程23
    2.5 總結摘要26
    CHAPTER 3 維持PATTERNS的正確-INCREMENTAL UPDATE27
    3.1問題描述27
    3.2 PAT INDEX的介紹28
    3.3 PAT INDEX在INCREMENTAL UPDATE的應用28
    3.3.1 First iteration29
    3.3.2 K-th iteration30
    3.4 多維 PATTERNS 的INCREMENTAL UPDATE32
    3.5 總結摘要33
    CHAPTER 4 實驗結果與討論34
    4.1 測試資料的產生34
    4.2 多維的PATTERN- MP1和MP2實驗數據37
    4.3多維PATTERN - INCREMENTAL UPDATE實驗數據41
    4.4 PAT_FUP的實驗數據43
    4-5總結摘要44
    CHAPTER5 結論與未來工作45
    5.1 結論 45
    5.2 未來工作46
    REFERENCE47
    APPENDIX A: PAT TREE ALGORITHM50
    Reference [1] Mike Perkowitz, Oren Etzioni: Adaptive Web Sites: an AI Challenge. IJCAI (1) 1997: 16-23
    [2] Rob Barrett, Paul P. Maglio, Daniel C. Kellem: How to Personalize the Web. CHI 1997: 75-82
    [3] O. R. Zaiane, M. Xin, J. Han, `` Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs', Proc. Advances in Digital Libraries Conf. (ADL'98), Santa Barbara, CA, April 1998, pp. 19-29.
    [4] Jones Kirsten L., nif-T-nav: A hierarchical navigator for WWW pages, Computer Networks And Isdn Systems (28)7-11 (1996) pp. 1345-1354,
    [5] Ming-Syan Chen, Jong Soo Park, Philip S. Yu: Efficient Data Mining for Path Traversal Patterns in Distributed Systems. ICDCS 1996: 385-393
    [6] T Sullivan. Reading reader reaction: A proposal for inferential analysis of web server log files. In Proc. 3rd Conf. Human Factors the Web, Denver, Colorado, June 1997.
    [7] O. R. Zaiane, M. Xin, J. Han, `` Discovering Web Access Patterns and Trends by Applying OLAP and Data Mining Technology on Web Logs', Proc. Advances in Digital Libraries Conf. (ADL'98), Santa Barbara, CA, April 1998, pp. 19-29.
    [8] M. Perkowitz and O. Etzioni. Adaptive sites: Au-Tomatically learning from user access patterns. In Proc. 6th Int. World Wide Web Conf., Santa Clara, California, April 1997.
    [9] Yan Tak Woon, Jacobsen Matthew, Garcia-Molina Hector, Dayal Umeshwar, From user access patterns to dynamic hypertext linking, Computer Networks And Isdn Systems (28)7-11 (1996) pp. 1007-1014
    [10] Stuart Schechter, Murali Krishnan, Michael D. Smith, Using path profiles to predict HTTP requests, Computer Networks (30)1-7 (1998) pp. 457-467
    [11] Rakesh Agrawal, Ramakrishnan Srikant: Mining Sequential Patterns. ICDE 1995: 3-14
    [12] Ramakrishnan Srikant, Rakesh Agrawal: Mining Sequential Patterns: Generalizations and Performance Improvements. EDBT 1996: 3-17
    [13] Jong Soo Park, Ming-Syan Chen, Philip S. Yu: An Effective Hash Based Algorithm for Mining Association Rules. SIGMOD Conference 1995: 175-186
    [14] David Wai-Lok Cheung, Jiawei Han, Vincent Ng, C. Y. Wong: Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique. ICDE 1996: 106-114
    [15] P.S.M. Tsai, C.C. Lee and A.L.P. Chen, "An Efficient Approach for Incremental Association Rule Mining," Proc. Pacific-Asia Conference on Knowledge Discovery and Data Mining.
    [16] R. Fuller and J. de Craaff. Measuring user motivation from server log files.
    [17] Mike Perkowitz, Oren Etzioni: Adaptive Web Sites: Automatically Synthesizing Web Pages. AAAI/IAAI 1998: 727-732
    [18] M. Perkowitz and O. Etzioni Towards Adaptive Web Sites: Conceptual Framework and Case Study in Proceedings of WWW8. 1999.
    [19] Ahmad M.Ahmad Wasfi; Collecting user access patterns for building user profiles and collaborative filtering; Proceedings of the 1999 international conference on Intelligent user interfaces , 1999, Pages 57 - 64
    [20] Fink, J.; Kobsa, A.; Schreck, J. (1997): Personalized Hypermedia Information Provision through Adaptive and Adaptable System Features: User Modeling, Privacy and Security Issues. Conference "IS&N 97", Como/Italy. ( Pkzipped Word 6.0, 195465 bytes)
    [21] Thorsten Joachims, Dayne Freitag, Tom M. Mitchell: Web Watcher: A Tour Guide for the World Wide Web. IJCAI (1) 1997: 770-777
    [22] Rakesh Agrawal, Heikki Mannila, Ramakrishnan Srikant, Hannu Toivonen, A. Inkeri Verkamo: Fast Discovery of Association Rules. Advances in Knowledge Discovery and Data Mining 1996: 307-328
    [23] Heikki Mannila, Hannu Toivonen, and A. Inkeri Verkamo: Discovery of frequent episodes in event sequences. Report C-1997-15, University of Helsinki,Department of Computer Science, February 1997. A revised version is to appear in Data Mining and Knowledge Discovery, 1997.
    [24] James E. Pitkow, Colleen M. Kehoe: Emerging Trends in the WWW User Population. CACM 39(6): 106-108 (1996)
    [25] MORRISON, D. 1968. “PATRICIA-Practical Algorithm to Retrieve Information Coded in Alphanumeric.” JACM, 15; 514-34
    [26] Oren Zamir, and Oren Etzioni .Web document clustering: a feasibility demonstration; Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval , 1998, Pages 46 – 54
    [27] Mike Perkowitz, Oren Etzioni: Adaptive Web Sites: an AI Challenge. IJCAI (1) 1997: 16-23
    Advisor
  • Baw-Jiune Liu(劉寶均)
  • Gwo-Dong Chen(陳國棟)
  • Files No Any Full Text File.
    Date of Submission

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