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研究生:余忠慶
研究生(外文):Chung-Ching Yu
論文名稱:多維度序列樣式挖掘之研究
指導教授:陳彥良陳彥良引用關係
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊管理研究所
學門:電算機學門
學類:電算機一般學類
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:51
中文關鍵詞:多維度序列序列樣式資料挖掘簡單格式
外文關鍵詞:Data MiningSequential PatternMulti-dimensional sequenceSimplified Format
相關次數:
  • 被引用被引用:4
  • 點閱點閱:204
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  • 收藏至我的研究室書目清單書目收藏:0
序列樣式(Sequential Pattern)的挖掘是資料挖掘一個相當重要的領域,但在以往的研究中,皆是針對以單一順序維度進行衡量的序列(Sequence)來進行探討,如消費者在商品購買上的順序行為,或是網路使用者對網頁的瀏覽順序等等。儘管這些研究可以解決實務上大多數的問題,但是,若序列中的項目屬性可以歸納至不同的時間概念層級,且所要找出的序列樣式是可以同時含括不同概念層級的順序性時,由於受限於以往方法的應用範圍,也因此,此類型的樣式並無法被尋出。而對於這種同時呈現多個順序維度的序列,我們即稱之為「多維度序列(multi-dimensional sequence)」。由於多維度序列是以「序列的序列(sequence of sequence)」、或是「序列的序列的序列(sequence of sequence of sequence)」等方式來加以呈現,故這類型序列樣式的挖掘方式也就不同於以往。因此在本文中,除了說明多維度序列的應用與相關定義之外,也提出一種簡化的表示方式,「簡單格式(Simplified Format)」,以進行序列的表示,並據以對現行的兩種序列挖掘演算法進行擴展,以尋出多維度的序列樣式。
第一章 緒論1
第二章 問題描述與相關定義5
第一節 問題描述5
第二節 多維度序列之定義7
第三節 相關延伸定義10
第四節 序列的簡單格式(SIMPLIFIED FORMAT)12
第三章 APRIORIMD方法18
第一節 候選序列(CANDIDATE)的產生19
第二節 支持次數(SUPPORT COUNT)之計算22
第三節 範例說明26
第四章 PREFIXMDSPAN方法29
第一節 相關定義29
第二節 演算法架構32
第三節 範例說明36
第五章 效能測試39
第一節 模擬資料的產生39
第二節 效能測試42
一、 不同的支持度43
二、 不同的資料量44
三、 不同維度數45
四、 不同的序列長度46
五、 不同的最大可能高頻率序列長度46
第三節 小結47
第六章 結論48
參考文獻49
1. R. Agrawal, C. Faloutsos and A. Swami, “Efficient Similarity Search in Sequence Databases,” In Lecture Notes in Computer Science 730, pp. 69-84, Springer Verlag, 1993.2. R. Agrawal, R. Srikant, “Fast algorithms for mining association rules,” In Proc. 1994 Int. Conf. Very Large Data Bases (VLDB’94), pp. 487-499, Santiago, Chile, Sep. 1994.3. R. Agrawal, R. Srikant, “Mining sequential patterns,” Research Report RJ 9910, IBM Almaden Research Center, San Jose, California, October 1994.4. R. Agrawal and R. Srikant, “Mining sequential patterns,” In Proc. 1995 Int. Conf. Data Engineering (ICDE’95), pp. 3-14, Taipei, Taiwan, Mar. 1995.5. M.S. Chen, J. Han, and P.S. Yu, “Data mining: an overview from a database perspective,” IEEE Transactions on Knowledge and Data Engineering, 8(6), pp. 866-883, 1996.6. M.S. Chen, J.S. Park and P.S. Yu, “Efficient data mining for Path Traversal Patterns,” In Proc. Of IEEE Trans. Knowledge and Data Engineering (IEEE’98), 10(2), pp. 209-221, March 1998.7. C. Faloutsos, M. Ranganathan and Y. Manolopoulos, “Fast Subsequence Matching in Time-Series Databases,” SIGMOD Conference, pp. 419-429, 1994.8. M. Garofalakis, R. Rastogi, and K. Shim, “Spirit: Sequential pattern mining with regular expression constraints,” In Proc. 1999 Int. Conf. Very Large Data Bases (VLDB’99), pp. 223-234, Edinburgh, UK, Sept, 1999.9. J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal and M-C. Hsu, “FreeSpan: Frequent Pattern-Projected Sequential Pattern Mining,” In Proc. 2000 Int. Conf. Knowledge Discovery and Data Mining (KDD’00), pp. 355-359, Boston, MA, Aug. 2000.10. J. Han, J. Pei and Y. Yin, “Mining Frequent Patterns without Candidate Generation,” In Proc. 2000 ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’00), pp. 1-12, Dallas, TX, May 2000.11. M.Y. Lin and S.Y. Lee, “Incremental Update On Sequential Patterns In Large Databases,” In Proc. Of the Tenth IEEE International Conference on Tools with Artificial Intelligence, 1998, pp.24-31.12. F. Masseglia, F. Cathala and P. Poncelet, “The PSP Approach for Mining Sequential Patterns,” In Proc. 1998 2nd European Symposium on Principles of Data Mining and Knowledge Discovery (PKDD’98), Vol. 1510, pp. 176-184, Nantes, France, LNAI, Sept. 1998.13. F. Masseglia, P. Poncelet, M. Teisseire, “Incremental Mining of Sequential Patterns in Large Database”, Actes des 16ièmes Journées Bases de Données Avancées (BDA’00), Blois, France, October 2000.14. H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovering Frequent Episodes in Sequences,” In Proc. 1995 Int. Conf. On Knowledge Discovery and Data Mining (KDD’95), pp. 210-215, Montreal, Canada, Aug 1995.15. H. Mannila, H. Toivonen, and A. I. Verkamo, “Discovery of Frequent Episode In Event Sequences,” Data Mining and Knowledge Discovery, No. 1, Nov. 1997, pp. 259-289.16. J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal and M-C. Hsu, “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth,” In Proc. 2001 Int. Conf. Data Engineering (ICDE’01), pp. 215-224, Heidelberg, Germany, April 2001.17. J. Pei, J. Han, Mortazavi-Asl, and H. Zhu, “Mining Access Patterns Efficiently from Web Logs,” In Proc. 2000 Pacific-Asia Conf. On Knowledge Discovery and Data Mining (PAKDD’00), pp. 396-407, Kyoto, Japan, April 2000.18. H. Pinto, J. Han, J.Pei, K. Wang, Q. Chen, and U.Dayal, “Multi-Dimensional Sequential Pattern Mining,” In Proc. 10th ACM International Conference on Information and Knowledge Management (CIKM’01), Altanta, Georgia, Nov. 2001.19. S. Parthasarathy, M. J. Zaki, M. Ogihara, S. Dwarkadas, “Incremental and Interactive Sequence Mining,” In Proc. of the 1999 ACM International Conference on Information and Knowledge Management (CIKM’99). Kansas City, Missouri. Nov. 1999.20. R. Srikant and R. Agrawal, “Mining sequential patterns: Generalizations and performance improvements,” In Proc. 5th Int. Conf. Extending Database Technology (EDBT’96), pp. 3-17, Avignon, France, Mar. 1996.21. M. J. Zaki, “SPADE: An Efficient Algorithm for Mining Frequent Sequences,” In Proc. of Machine Learning Journal, special issue on Unsupervised Learning (Doug Fisher, ed.), Vol. 42 Nos. 1/2, pp. 31-60, Jan/Feb 2001.22. K. Wang, and J. Tan, “Incremental Discovery of Sequential Patterns,” In 1996 ACM-SIGMOD Data Mining Workshop: Research Issues on Data Mining and Knowledge Discovery (SIGMOD’96), pp. 95-102, Montreal, Canada, May 1996.
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