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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 | Mao-Hsu Chen | en |
dc.contributor.author | 陳妙昕 | zh_TW |
dc.date.accessioned | 2021-06-15T05:45:08Z | - |
dc.date.available | 2013-08-20 | |
dc.date.copyright | 2010-08-20 | |
dc.date.issued | 2010 | |
dc.date.submitted | 2010-08-19 | |
dc.identifier.citation | [1] R. Agrawal and R. Srikant, Fast algorithms for mining association rules, Proceedings of the 20th Very Large Data Base Conference, 1994, pp. 487-99.
[2] J, Ayres, J. E. Gehrke, T. Yiu, J. Flannick, Sequential pattern mining using a bitmap representation, Proceedings of ACM SIGMOD International Conference on Knowledge Discovery in Database, 2002, pp. 429-435. [3] H. Cao, N. Mamoulis, D.W. Cheung, Mining frequent spatio-temporal sequential patterns, Proceedings of the International Conference on Data Mining, 2005, pp. 82-89. [4] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, M. C. Hsu, FreeSpan: Frequent pattern-projected sequential pattern mining, Proceedings of International Conference on Knowledge Discovery and Data Mining, 2000, pp. 355-359. [5] K. Koperski, J. Han, Discovery of spatial association rules in geographic information databases, Proceeding of the International Symposium on Large Spatial Databases, 1995, pp. 47-66. [6] M. Leleu, C. Rigotti, J. F. Boulicaut, G. Euvrard, GO-SPADE: Mining sequential patterns over datasets with consecutive repetitions, Proceedings of International Conference on Machine Learning and Data Mining, 2001, pp. 293-306. [7] X. Li, J. Han, S. Kim, Motion-alert: Automatic anomaly detection in massive moving objects, Proceedings of the IEEE International Conference on Intelligence and Security Informatics, 2006, pp. 166-177. [8] N. Pasquier, Y. Bastide, R. Taouil, L. Lakhal, Discovering frequent closed itemsets for association rules, Proceedings of the 7th International Conference on Database Theory, Jerusalem, Israel, 1999, pp. 398-416. [9] J. Pei, J. Han, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, Mining frequent patterns without candidate generation, Proceedings of ACM-SIGMOD International Conference on Management of Data Mining, 2000, pp. 1-12. [10] J. Pei, J. Han, and R. Mao, CLOSET: An efficient algorithm for mining frequent closed itemsets, Proceedings of the 5th ACM-SIGMOD Workshop on Research Issues in Data Mining and Knowledge Discovery, 2000, pp. 11-20. [11] J. Pei, J. Han, B. Mortazavi-Asl, Q. Chen, U. Dayal, M. C. Hsu, PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, Proceedings of the IEEE International Conference on Data Engineering, 2001, pp. 215-224. [12] R. Srikant, R. Agrawal, Mining sequential patterns: Generalizations and performance improvements, Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, 1996, pp. 3-17. [13] J. Wang, J. Han, and J. Pei, CLOSET+: Searching for the best strategies for mining frequent closed itemsets, Proceedings of the International Conference on Knowledge Discovery and Data Mining, 2003, pp. 236-245. [14] X. Yan, J. Han, and R. Afshar, CloSpan: Mining closed sequential patterns in large datasets, Proceedings of the 2003 SIAM International Conference on Data Mining, 2003, pp. 166-177. [15] M. J. Zaki, Scalable algorithms for association mining, IEEE Transactions on Knowledge and Data Engineering, Vol. 12, No. 3, 2000, pp. 372-390. [16] M. J. Zaki, SPADE: An efficient algorithm for mining frequent sequences, Machine Learning, Vol. 42, No. 1, 2001, pp. 31-6. [17] M. J. Zaki, and C. Hsiao, Efficient algorithms for mining closed itemsets and their lattice structure, IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 4, 2005, pp. 462-478. [18] X. Zhang, N. Mamoulis, D.W. Cheung, Y. Shou, Fast mining of spatial collocations, Proceeding of the ACM SIGKDD International Conference on Knowledge Discovery in Databases, 2004, pp. 384-393. | |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/47007 | - |
dc.description.abstract | 時空資料庫中樣式的資料探勘,可以幫助我們了解地理位置上分布不同的目標物或是事件的連續變化趨勢。因此,在這篇論文中,我們提出一個有效率的探勘演算法叫「STP-Mine」,用來找尋時空資料庫中的封閉性數值樣式。演算法主要可分為三個階段。第一階段,我們產生出所有長度為1的頻繁樣式及其映射資料庫。第二階段,我們利用頻繁樣式樹在空間維度上以深先搜尋法的方式遞迴產生所有的頻繁樣式。第三階段,我們利用頻繁樣式樹在時間維度上以深先搜尋法的方式遞迴產生所有的頻繁樣式。第二、第三階段的步驟將不斷地遞迴進行,直到沒有頻繁時空樣式被產生為止。在探勘的過程中,我們利用一些有效的修剪策略,以避免產生不必要的候選樣式,並檢查所產生的樣式是否為封閉的樣式。實驗結果顯示,不論在合成資料或真實資料中,我們所提出的方法皆優於改良式的A-Close演算法。 | zh_TW |
dc.description.abstract | Mining spatial-temporal patterns can help us retrieve valuable and implicit information from an abundance of spatial-temporal data in a database. In this thesis, we propose a novel algorithm, STP-Mine (Spatial- Temporal Patterns-Mine), to mine closed stpatterns in a spatial-temporal database. The proposed algorithm consists of three phases. First, we find all frequent length-1 patterns (1-patterns) and construct a projected database for each frequent 1-pattern found. Second, we recursively generate frequent super-patterns in the spatial dimension in a depth-first search manner. Third, once a pattern cannot grow further in the spatial dimension, we extend it in the temporal dimension in a depth-first search manner. The steps in the second and third phases are repeated until no more frequent closed patterns can be found. During the mining process, we employ several effective pruning strategies to prune unnecessary candidates and a closure checking scheme to remove non-closed stpatterns. The experimental results show the STP-Mine algorithm is efficient and scalable, and outperforms the modified A-Close algorithm in one order of magnitude. | en |
dc.description.provenance | Made available in DSpace on 2021-06-15T05:45:08Z (GMT). No. of bitstreams: 1 ntu-99-R97725008-1.pdf: 535242 bytes, checksum: 0a4432cd61309a6c1f05002806b2dff5 (MD5) Previous issue date: 2010 | en |
dc.description.tableofcontents | Table of Contents i
List of Figures ii List of Tables iii Chapter 1 Introduction 1 Chapter 2 Related Work 4 Chapter 3 Preliminaries and Problem Definitions 6 Chapter 4 The Proposed Algorithm 11 4.1 Frequent 1-Spatial Pattern Generation 11 4.2 Spatial Pattern Expansion 13 4.3 Spatial-Temporal Pattern Expansion 15 4.4 Closure Checking and Pruning Strategies 17 4.5 The STP-Mine algorithm 19 4.6 An Example 22 Chapter 5 Performance Analysis 26 5.1 Synthetic Datasets 27 5.2 Performance Evaluation on Synthetic Datasets 28 5.3 Performance Evaluation on Real Datasets 32 Chapter 6 Conclusions and Future Work 36 | |
dc.language.iso | en | |
dc.title | 時空資料庫中封閉性數值樣式之資料探勘 | zh_TW |
dc.title | Mining Closed Numerical Patterns in Spatial-Temporal Databases | en |
dc.type | Thesis | |
dc.date.schoolyear | 98-2 | |
dc.description.degree | 碩士 | |
dc.contributor.oralexamcommittee | 呂永和,劉敦仁 | |
dc.subject.keyword | 資料探勘,時空資料庫,封閉性樣式,頻繁樣式, | zh_TW |
dc.subject.keyword | data mining,spatial-temporal database,closed pattern,frequent pattern, | en |
dc.relation.page | 40 | |
dc.rights.note | 有償授權 | |
dc.date.accepted | 2010-08-19 | |
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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