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完整後設資料紀錄
DC 欄位 | 值 | 語言 |
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dc.contributor.advisor | 李瑞庭 | |
dc.contributor.author | Jen-Feng Li | en |
dc.contributor.author | 李任峰 | zh_TW |
dc.date.accessioned | 2021-06-13T07:52:25Z | - |
dc.date.available | 2005-07-28 | |
dc.date.copyright | 2005-07-28 | |
dc.date.issued | 2005 | |
dc.date.submitted | 2005-07-25 | |
dc.identifier.citation | [1] R. Agrawal, T. Imielinski, A. Swami, Mining association rules between sets of items in large databases, In Proceedings of ACM-SIGMOD, 1993, pp. 207-216.
[2] R. Agrawal and R. Srikant, Fast algorithms for mining association rules, In Proc. of Int. Conf. Very Large Data Bases (VLDB’94), Santiago, Chile, Sept. 1994, pp. 487-499. [3] R. Agrawal and R. Srikant, Mining sequential patterns, In Proc. of Int. Conf. Data Engineering (ICDE’95), Taipei, Taiwan, Mar. 1995, pp. 3-14. [4] J. Ayres, J. E. Gehrke, T. Yiu, and J. Flannick, Sequential pattern mining using bitmaps, In Proc. of ACM SIGKDD Int. Conf. Knowledge Discovery in Databases (KDD’02), Edmonton, Canada, July 2002. [5] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M.-C. Hsu, Freespan: frequent pattern-projected sequential pattern mining, In Proc. of Int. Conf. Knowledge Discovery and Data Mining (KDD’00), Boston, MA, August 2000, pp. 355-359. [6] J. Han, J. Pei, and Y. Yin, Mining frequent patterns without candidate generation, In Proc. of ACM-SIGMOD Int. Conf. Management of Data (SIGMOD’00), Dallas, TX, May 2000, pp. 1-12. [7] M. Leleu, C. Rigotti, J.-F. Boulicaut, and G. Euvrard, GO-SPADE: mining sequential patterns over datasets with consecutive repetitions, In Proc. of Int. Conf. Machine Learning and Data Mining (MLDM’03), Leipzig, Germany, July 2003, pp. 293-306. [8] F. Masseglia, F. Cathala, and P. Poncelet, The psp approach for mining sequential patterns, In Proc. of 1998 European Symp. Principle of Data Mining and Knowledge Discovery (PKDD’98), Nantes, France, September 1998, pp. 176-184. [9] H. Mannila, H. Toivonen, and A. I. Verkamo, Discovery of frequent episodes in event sequences, Data mining and Knowledge Discovery, vol. 1, no. 3, 1997, pp. 259-289. [10] N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering frequent closed itemsets for association rules, In Proc. of 7th Int. Conf. Database Theory (ICDT’99), Jerusalem, Israel, January 1999, pp. 398-416. [11] 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. of 2001 Int. Conf. Data Engineering (ICDE’01), Heidelberg, Germany, April 2001, pp. 215-224. [12] R. Srikant and R. Agrawal, Mining sequential patterns: generalizations and performance improvements, In Proc. of 1996 Int. Conf. Extending Database Technology (EDBT’96), Avignon, France, Mar 1996. [13] E. Ukkonen, On-line construction of suffix trees, Algorithmica, vol. 14, no. 13, 1995, pp. 249-260. [14] J. Wang, J. Han, BIDE: efficient mining of closed sequences, In Proc. of 2004 Int. Conf. Data Engineering (ICDE’04), Boston, Massachusetts, April 2004, pp. 79-90. [15] J. Wang, J. Han, and J. Pei, CLOSET+: searching for the best strategies for mining frequent closed itemsets, In Proc. Int. of Conf. Knowledge Discovery and Data Mining (KDD’03), Washington, DC, August 2003, pp. 236-245. [16] X. Yan, J. Han, and R. Afshar, CloSpan: mining closed sequential patterns in large databases, In Proc. of 2003 SIAM Int. Conf. Data Mining (SDM’03), San Francisco, CA, May 2003. [17] M. J. Zaki, SPADE: an efficient algorithm for mining frequent sequences, Machine Learning, Vol. 1, No. 1~2, 2001, pp. 31-60. [18] M. Zaki, and C. Hsiao, CHARM: an efficient algorithm for closed itemset mining, In Proc. of SIAM Int. Conf. Data Mining (SDM’02), Arlington, VA, April 2002, pp. 457-473. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/36149 | - |
dc.description.abstract | Discovering association rules can reveal the cause-effect relationships among events in a time-series database. The problem can be transformed to finding frequent sequential patterns. However, most of sequential pattern mining algorithms proposed are not suitable to mine frequent patterns in a time-series database since they are not efficient to mine frequent patterns for long sequences and a time-series database usually contains long sequences. Moreover, they do not consider the distance between the frequent patterns. Thus, in this thesis, we propose an efficient algorithm to mine frequent patterns in time-series database.
Our proposed algorithm, CP-Miner, consists of three phases. First of all, we transform every real value number in a time-series sequence into a symbolic level so that every time-series sequence can be considered as a string. Then we employ a suffix tree to store the whole database thus we can easily find the frequent strings by traversing the suffix tree. Finally, we can combine these frequent strings to generate longer frequent patterns by traversing the suffix tree. It is shown that the CP-Miner algorithm outperforms the Apriori-like algorithm in terms of runtime and space requirement. | en |
dc.description.provenance | Made available in DSpace on 2021-06-13T07:52:25Z (GMT). No. of bitstreams: 1 ntu-94-R92725037-1.pdf: 401385 bytes, checksum: 412400b65db9020b031eed74f05be3d0 (MD5) Previous issue date: 2005 | en |
dc.description.tableofcontents | Table of Contents i
List of Figures ii List of Tables iii Chapter 1 Introduction 1 Chapter 2 Literature Survey 3 2.1 AprioriAll Algorithm 3 2.2 PrefixSpan 5 2.3 Discussion 7 Chapter 3 Mining Association Rules in Time-series Databases 9 3.1 Problem Definition and Notations 9 3.2 Suffix Tree 10 3.3 Our Proposed Method 12 3.3.1 Quantization phase 12 3.3.2 Discovering phase 12 3.3.3 Combination phase 15 Chapter 4 Performance Evaluation and Experimental Results 28 4.1 Synthetic Data 28 4.2 Real Data 31 Chapter 5 Conclusions and Future Work 35 References 36 | |
dc.language.iso | en | |
dc.title | 在時間序列資料庫中探勘關聯性規則 | zh_TW |
dc.title | Mining Association Rules in Time-series Databases | en |
dc.type | Thesis | |
dc.date.schoolyear | 93-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 劉敦仁,沈錳坤 | |
dc.subject.keyword | 資料探勘,關聯性規則,時間序列資料庫,字尾樹, | zh_TW |
dc.subject.keyword | data mining,association rules,time-series databases,suffix tree, | en |
dc.relation.page | 37 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2005-07-25 | |
dc.contributor.author-college | 管理學院 | zh_TW |
dc.contributor.author-dept | 資訊管理學研究所 | zh_TW |
顯示於系所單位: | 資訊管理學系 |
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