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研究生:嚴久欽
研究生(外文):Chiu-Chin Yen
論文名稱:實作序列型樣探勘系統並應用到網站日誌
論文名稱(外文):Implement A Sequential Patterns Mining System And Apply To Web Log
指導教授:許清琦許清琦引用關係
指導教授(外文):Ching-Chi Hsu
學位類別:碩士
校院名稱:國立臺灣大學
系所名稱:資訊工程學研究所
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2002
畢業學年度:90
語文別:中文
論文頁數:68
中文關鍵詞:序列型樣
外文關鍵詞:Sequential Patterns
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建立一套序列型樣探勘系統(Sequential Patterns Mining System),其序列型樣探勘系統可以挖掘出所有符合最小支持度(Minimum Support)的序列型樣並可以產生序列關聯規則(Interesting Rules)。
此序列型樣探勘系統也定義了一套序列探勘查詢語言(Sequential Mining Query Language),可以查詢感興趣的序列型樣並可以產生感興趣的序列關聯規則(Interesting Rules),查詢語言可以接受萬用字元(Wildcard),可以更方便的查出一想不到的探勘結果。
藉由這套序列型樣探勘系統中的序列型樣探勘引擎(Sequential Patterns Mining Engine-SPME),我們應用到網站日誌探勘(Web Log Mining)中,分析網站使用者的瀏覽行為並找出有趣的規則,藉由這有趣的規則提供網站管理者進一步改善網站網頁編排及網頁推薦。
第一章 緒論
1.1 前言 1
1.2 研究動機 1
1.3 研究目的 2
1.4 內容簡介 2
第二章 理論基礎
2.1 關聯規則(Association Rules)之探勘 4
2.2 關聯規則之探勘基本定義 5
2.3 關聯規則之探勘程序 9
2.4 關聯規則之探勘Apriori演算法 10
2.5 序列型樣(Sequential Patterns)探勘 13
2.6 序列型樣探勘之基本定義 14
2.7 序列關聯規則的產生 17
2.8 序列型樣及關聯規則之探勘程序 22
2.9 序列型樣探勘Apriori-like演算法 23
第三章 序列型樣探勘系統建立
3.1 序列型樣探勘系統架構 30
3.2 序列型樣探勘引擎-SPME 32
3.2.1 序列型樣探勘PrefixSpan演算法 33
3.2.2 XML規格及DOM技術 43
3.3 資料來源格式轉換介面 47
3.4 序列型樣探勘系統之運作程序 48
3.5 序列關聯規則的管理 49
第四章 序列探勘查詢語言
4.1 序列探勘查詢語言-SMQL 51
4.1.1 序列探勘查詢語言語法定義 53
4.1.2 序列探勘查詢語言語法剖析器 55
4.2 序列探勘查詢之運作程序 57
第五章 實作結果與應用
5.1 序列型樣的應用領域 58
5.2 探勘查詢語言應用實例及結果 59
5.3 網站日誌探勘器-WLMiner 61
第六章 結論與未來工作
6.1 結論 65
6.2 未來工作 66
參考文獻
[1] R. Agrawal and R. Srikant. ''''Fast Algorithms for Mining Association Rules in Large Databases'''' Proc. of the 20th Int’l Conference on Very large Databases, pp.487-499, Santiago, Chile, Sep 1994
[2] R. Agrawal, R. Srikant: "Mining Sequential Patterns", Proc. of the Int''l Conference on Data Engineering (ICDE), Taipei, Taiwan, March 1995. Expanded version available as IBM Research Report RJ9910, October 1994.
[3] R. Agrawal, C. Faloutsos, A. Swami: "Efficient Similarity Search in Sequence Databases", Proc. of the 4th Int''l Conference on Foundations of Data Organization and Algorithms, Chicago, Oct. 1993, Also in Lecture Notes in Computer Science 730, Springer Verlag, 1993, 69-84.
[4] R. Agrawal, K. Lin, H. S. Sawhney, K. Shim: "Fast Similarity Search in the Presence of Noise, Scaling, and Translation in Time-Series Databases," Proc. of the 21st Int''l Conference on Very Large Databases, Zurich, Switzerland, September 1995
[5] Bettini, C.; Wang, X.S.; Jajodia, S.; Lin, J.-L.; "Discovering frequent event patterns with multiple granularities in time sequences ," IEEE Transactions on Knowledge and Data Engineering, Volume: 10 Issue: 2 , March-April 1998 Page(s): 222 -237
[6] R. J. Bayardo Jr." Efficiently Mining Long Patterns from Databases" In Proc. of the 1998 ACM-SIGMOD Int''l Conf. on Management of Data,85-93, 1998.
[7] M-S. Chen, J-S. Park and P. S. Yu, ''''Using a Hash-Based Method with Transaction Trimming for Mining Association Rules,'''' IEEE Trans. on Knowledge and Data Engineering, Vol. 9, No. 5, Oct. 1997, pp. 813-825.
[8] M.-S. Chen, J.-S. Park and P. S. Yu, ''''Efficient Data Mining for Path Traversal Patterns,'''' IEEE Trans. on Knowledge and Data Engineering, Vol. 10, No. 2, pp. 209-221, Arpil 1998.
[9] Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava, "Web Mining: Information and Pattern Discovery on the World Wide Web," (A Survey Paper) (1997), in Proceedings of the 9th IEEE International Conference on Tools with Artificial Intelligence (ICTAI''97), November 1997.
[10] Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava, "Grouping Web Page References into Transactions for Mining World Wide Web Browsing Patterns ," Proceedings of the 1997 IEEE Knowledge and Data Engineering Exchange Workshop (KDEX-97), November 1997.
[11] Robert Cooley, Bamshad Mobasher, and Jaideep Srivastava, "Data Preparation for Mining World Wide Web Browsing Patterns," Journal of Knowledge and Information Systems, Vol. 1, No. 1, 1999
[12] Christos Faloutsos, M. Ranganathan, Yannis Manolopoulos, "Fast Subsequence Matching in Time-Series Databases," SIGMOD Conference 1994: 419-429
[13] J. Han, J. Pei and Y. Yin, "Mining Frequent Patterns without Candidate Generation," Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD''00), Dallas, TX, May 2000, pp. 1-12.
[14] J. Han J. Pei, B. Mortazavi-Asi, Q. Chen, U. Dayal, M.C. Hsu, ''''FreeSpan: Frequent pattern-projected sequential pattern mining, ''''KDD’00.
[15] J. Han, J. Pei, H. Pinto, Q. Chen, U. Dayal, and M.-C. Hsu, ''''PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth'''', Proc. of 2001 Int’l Conference on Data Engineering (ICDE''01), Heidelberg, Germany, April 2001
[16] J. Han, J. Pei, B. Mortazavi-Asl, and H. Zhu '''' Mining Access Pattern efficiently from Web logs '''', Proc. 2000 Pacific-Asia Conf. on Knowledge Discovery and Data Mining (PAKDD''00), Kyoto, Japan, April 2000.
[17] J. Han, O. R. Zaiane, M. Xin, "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.
[18] S. Li, H. Shen and L. Cheng, "New Algorithms For Efficient Mining Of Association Rules," Information Sciences, Vol. 118, No. 1-4, Sep. 1999, pp. 251-268.
[19] Ming-Yen Lin; Suh-Yin Lee, "Incremental update on sequential patterns in large databases," Proceedings. Tenth IEEE International Conference on Tools with Artificial Intelligence, 1998., 1998 Page(s): 24 -31
[20] B. Mobasher, N. Jain , E. Han and J. Srivastava, "Web Mining: Pattern Discovery from World Wide Web Transactions," Technical Report TR96-050, Department of Computer Science, University of Minnesota, 1996
[21] R. Meo, G. Psaila, and S. Ceri. ''''A New SQL-like Operator for Mining Association Rules. '''' In Proceedings of the International Conference on Very Large Data Bases, pages 122-133, 1996.
[22] Park, S.; Lee, D.; Chu, W.W. , "Fast retrieval of similar subsequences in long sequence databases," Proceedings. 1999 Workshop on Knowledge and Data Engineering Exchange, 1999. (KDEX ''99)., Page(s): 60 -67
[23] N. Pasquier, Y. Bastide, R. Taouil and L. Lakhal, "Efficient Mining Of Association Rules Using Closed Itemset Lattices," Information Systems, Vol. 24, No. 1, March 1999, pp. 25-46.
[24] Ramirez, J.C.G.; Cook, D.J.; Peterson, L.L.; Peterson, D.M., "Temporal pattern discovery in course-of-disease data," IEEE Engineering in Medicine and Biology Magazine , Volume: 19 Issue: 4 , July-Aug. 2000 Page(s): 63 -71
[25] A. Savasere, E. Omiecinski and S. Navathe, "An Efficient Algorithm for Mining Association Rules in Large Databases," Proc. Int''l Conf. Very Large Data Bases, Zurich, Switzerland, Sep. 1995, pp. 432-444
[26] Feng Tao; Murtagh, K., "Towards knowledge discovery from WWW log data," Proceedings. International Conference on Information Technology: Coding and Computing, 2000, Page(s): 302 -307
[27] Interactive exploration of interesting findings in the TelecommunicationNetwork Alarm Sequence Analyzer (TASA) Toivonen, H.; Klemettinen, M.; Mannila, H., Information and Software Technology, Volume: 41,Issue: 9, June 25, 1999, pp. 557-567
[28] Hannu Toivonen, Heikki Mannila, and A. Inkeri Verkamo.,"Discovery of frequent episodes in event sequences," Data Mining and Knowledge Discovery, 1(3): 259 -289, November 1997.
[29] H. Toivonen, "Sampling Large Databases For Association Rules," The 22th International Conference on Very Large Databases (VLDB''96), Mumbay, India, Sep. 1996, pp. 134-145
[30] Fan-Chen Tseng and Ching-Chi Hsu, "Generating Frequent Patterns with the Frequent Pattern List," to appear in Proceedings of the Fifth Pacific-Asia Conference on Knowledge Discovery and Data Mining, April, 2001.
[31] Show-Jane Yen and Arbee L.P. Chen. An Efficient Approach to Discovering Knowledge from large Databases. In PDIS, pages 8-18, 1996
[32] Yi, B.-K.; Sidiropoulos, N.D.; Johnson, T.; Jagadish, H.V.; Faloutsos, C.; Biliris, A., "Online data mining for co-evolving time sequences," Proceedings. 16th International Conference on Data Engineering 2000., Page(s): 13 -22
[33] Mohammed J. Zaki, "Efficient Enumeration of Frequent Sequences, " 7th International Conference on Information and Knowledge Management, pp 68-75, Washington DC, November 1998.
[34] World Wide Web Consortium. ''''Extensible Markup Language (XML) ''''http://www.w3.org/XML
[35] World Wide Web Consortium. ''''Document Object Model (DOM) ''''http://www.w3.org/DOM
[36] Microsoft XML Parser. Support of the World Wide Web (W3) Consortium final recommendation for XML Schema, with both DOM and SAX.http://msdn.microsoft.com/XML
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