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系統識別號 U0002-0708200715372000
中文論文名稱 挖掘模糊時間序列型樣
英文論文名稱 Mining Fuzzy Time Sequential Patterns
校院名稱 淡江大學
系所名稱(中) 資訊工程學系碩士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 95
學期 2
出版年 96
研究生中文姓名 廖珮妤
研究生英文姓名 Pei-Yu Liao
學號 693190547
學位類別 碩士
語文別 中文
口試日期 2007-06-14
論文頁數 60頁
口試委員 指導教授-林丕靜
委員-王鄭慈
委員-蔣定安
中文關鍵字 序列型樣  階層式分群法  模糊數  模糊時間區間 
英文關鍵字 Sequential patterns  Hierarchical clustering  Fuzzy number  Fuzzy time interval 
學科別分類 學科別應用科學資訊工程
中文摘要   挖掘序列型樣是資料挖掘中一項重要的技術,主要用於找出序列資料庫中的頻繁子序列。一般挖掘序列型樣的演算法大多能正確找出頻繁項目集之間的發生先後順序,但對於其出現前後的間隔時間卻無法加以描述。
  為了有效解決此一問題,本研究結合階層式分群法與模糊數的概念,提出一個挖掘模糊時間序列型樣的演算法,不僅可以挖掘出序列資料庫中所有頻繁項目及其出現的順序關係,更可將其出現的間隔時間以合理的方式表示。
英文摘要 An important task of sequential patterns mining is to discover frequent sequential patterns in a sequence database. Conventional sequential patterns only reveal the order of items, information about time intervals between successive by occurred items has not been determined.
In this paper, we proposed an algorithm called fuzzy time sequential pattern mining (FTSP). We use the hierarchical clustering technique to cluster the time intervals between successive itemsets, and define a fuzzy number to each time cluster to compute the fuzzy support, and then we have mined the frequent fuzzy time sequential patterns.
Fuzzy time sequential patterns mining, reveals not only the order of items, but also the time intervals between successive items.
論文目次 目錄 Ⅰ
表目錄 Ⅲ

第1章 緒論 1
  1.1 研究動機與目的 1
  1.2 論文架構 2
第2章 資料挖掘與序列型樣 3
  2.1 資料挖掘 3
  2.2 序列型樣 12
  2.3 時間序列型樣 21
第3章 模糊時間序列型樣之研究 24
  3.1 資料分群法 24
  3.2 階層式分群法 27
  3.3 模糊集合論 29
  3.4 模糊數 30
  3.5 模糊時間序列型樣 35
第4章 模糊時間序列型樣之實驗 40
  4.1 模糊時間序列型樣演算法運算步驟 40
  4.2 實驗結果 50
第5章 結論 51
  5.1 結論 51
  5.2 未來研究方向 52
參考文獻 53
附錄—英文論文 55

表目錄

Table 4.1 Sequence database 40
Table 4.2 Itemset and its support 41
Table 4.3 Candidate 2-squences, C2 42
Table 4.4 Frequent 2-squences, L2 43
Table 4.5 Time-clusters of frequent 2-sequence and its frequency 44
Table 4.6 Fuzzy number representation and fsupp of each time cluster 45
Table 4.7 Fuzzy number of frequent 2-sequence, L2 46
Table 4.8 Frequent fuzzy time 2-sequences, L2’ 47
Table 4.9 Candidate frequent fuzzy time 3-sequences, C3’ 48
Table 4.10 Frequent fuzzy time 3-sequences, L3’ 49
Table 4.11 Candidate frequent fuzzy time 4-sequences, C4’ 49
參考文獻 [1] R. Agrawal and R. Srikant, “Mining Sequential Patterns,” Proceedings. of the Inernational Conference on Data Engineering(ICDE), (March 1995).
[2] R. Agrawal and R. Srikant, ‘‘Mining Sequential Patterns: Generalization And Performance Improvement,’’ Proc. 5th Int. Conference on Extending Database Technology, pp. 3-17, (1996).
[3] Han, J., Kamber, M., Data mining: Concepts and Techniques, Academic Press, (2001).
[4] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. Hsu, ‘‘FreeSpan: frequent pattern-projected sequential pattern mining, ’’ Proc. Int. Conf. on Knowledge Discovery and Data Mining, (2000).
[5] J. Pei, J. Han, B. Mortazavi-Asl, H. Ping, Q.Chen, U. Dayal, and M. –C Hsu, ‘‘PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Project Pattern Growth’’ In. Proc. 2001 Int. Conf. Data Engineering (ICDE’01), pp. 215-224, (2001).
[6] Klir, G. J., Yuan, B., Fuzzy sets and Fuzzy Logic’, Theory and Applications, Prentice Hall PTR, (1995).
[7] Mannila, H., Toivonen, H., Inkeri Verkamo, A., ‘‘Discovery of frequent episodes in event sequences,’’ Data Mining and Knowledge Discovery, 1(3), pp. 259-289, (1997).
[8] M. N. Garofalakis, R. Rastogi, and K. Shim, ‘‘SPIRIT: Sequential Pattern Mining with Regular Expression Constraints,’’ Proc. Int. Conf. on Very Large Data Bases (VLDB), pp. 223-234, (1999).
[9] P. C. Wong, W. Cowley, H. Foote, E. Jurrus, and J. Thomas, ‘‘Visualizing sequential patterns for text mining,’’ Pacific Northwest National Laboratory. In Proceedings of IEEE Information Visualization, (2000).
[10] Yen-Liang Chen,*, Mei-Ching Chiang, Ming-Tat Kob, ‘‘Discovering time-interval sequential patterns in sequence databases,’’ Expert Systems with Applications, 25, pp.343–354, (2003).
[11] Yen-Liang Chen and Tony Cheng-Kui Huang, ‘‘Discovering Fuzzy Time-Interval Sequential Patterns in Sequence Databases,’’ IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS— PART B: CYBERNETICS, VOL. 35, NO. 5, (Octobers 2005).
[12] Wu, P.-H, Peng, W.-C., Chen, M.-S., ‘‘Mining sequential alarm patterns in a telecommunication database,’’ Proceedings of Workshop on Databases in Telecommunications (VLDB 2001), pp. 37-51, (2001).
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