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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李瑞庭 | |
| dc.contributor.author | Yi-Yu Lai | en |
| dc.contributor.author | 賴奕伃 | zh_TW |
| dc.date.accessioned | 2021-05-20T20:30:24Z | - |
| dc.date.available | 2011-02-03 | |
| dc.date.available | 2021-05-20T20:30:24Z | - |
| dc.date.copyright | 2009-02-03 | |
| dc.date.issued | 2008 | |
| dc.date.submitted | 2008-07-30 | |
| dc.identifier.citation | [1] R. Agrawal and R. Srikant, Mining sequential patterns, in Proceedings of the Eleventh International Conference on Data Engineering, Taipei, Taiwan, 1995, pp. 3-14.
[2] J, Ayres, J. E. Gehrke, T. Yiu, and J. Flannick, Sequential pattern mining using a bitmap representation, in Proceedings of ACM SIGMOD International Conference on Knowledge Discovery in Database, Edmonton, Canada, 2002, pp. 429-435. [3] J. Cheng, Y. Ke, and W. Ng, δ-Tolerance Closed Frequent Itemsets, Proceedings of the IEEE International Conference on Data Mining, Hong Kong, China, 2006, pp. 139-148. [4] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, Mining frequent patterns without candidate generation, in Proceedings of ACM-SIGMOD International Conference on Management of Data Mining, 2000, pp. 1-12. [5] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, FreeSpan: frequent pattern-projected sequential pattern mining, in Proceedings of International Conference on Knowledge Discovery and Data Mining, 2000, pp.355-359. [6] J. Huan, W. Wang, and J. Prins, Efficient mining of frequent subgraphs in the presence of isomorphism, in Proceedings of IEEE International Conference on Data mining, 2003, pp. 549-552. [7] A. Inokuchi, T. Washio, and H.Motoda, An Apriori-based algorithm for miningfrequent substructures from graph data, in Proceedings of European Conference on Principles and Practice of Knowledge in Databases, 2000, pp. 13-23. [8] R. Jin, C. Wang, D. Polshakov, S. Parthasarathy, G. Agarwal, Discovery frequent topological structures from graph datasets, in Proceeding of the Eleventh ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2005, pp. 606-611. [9] M. Kuramochi and G. Karypis, Frequent subgraph discovery, in Proceedings of IEEE International Conference on Data Mining, 2001, pp. 313-320. [10] M. Leleu, C. Rigotti, Jean-Francois Boulicaut, and G. Euvrard, GO-SPADE: mining sequential patterns over datasets with consecutive repetitions, in Proceedings of International Conference on Machine Learning and Data Mining, 2001, pp. 293-306. [11] C. Lucchese, S. Orlando, and R. Perego, Fast and memory efficient mining of frequent closed itemsets, IEEE Transactions on Knowledge and Data Engineering, Vol. 18, No. 1, 2006, pp. 21-36. [12] N. Pasquier, Y. Bastide, R. Taouil, and L. Lakhal, Discovering frequent closed itemsets for association rules, Proceedings of the 7th International Conference on Database Theory, Jerusalem, Israel, 1999, pp. 398-416. [13] 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, Dallas, USA, 2000, pp. 11-20. [14] J. Pei, J. Han, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. C. Hsu, PrefixSpan: mining sequential patterns efficiently by prefix-projected pattern growth, in Proceedings of IEEE International Conference on Data Engineering, 2001, pp. 215-224. [15] N.G. Singh, S. R. Singh, and A.K. Mahanta, CloseMiner: discovering frequent closed itemsets using frequent closed tidsets, Proceedings of IEEE International Conference on Data Mining, Houston, USA, 2005, pp. 633-636. [16] R. Srikant and R. Agrawal, Mining sequential patterns: generalizations and performance improvements, in Proceedings of the 5th International Conference on Extending Database Technology: Advances in Database Technology, 1996, pp. 3-17. [17] T. Uno, T. Asai, Y. Uchida, and H. Arimura, An efficient algorithm for enumerating closed patterns in transaction databases, Proceedings of the 7th International Conference on Discovery Science, Padova, Italy, 2004, pp. 16-31. [18] 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, Washington, D.C., USA, 2003, pp. 236-245. [19] X. Yan and J. Han, gSpan: graph-based substructure pattern mining, in Proceedings of International Conference on Data Mining, 2002, pp. 721-724 [20] M. J. Zaki, SPADE: an efficient algorithm for mining frequent sequences, Machine Learning, Vol. 42, No. 1, 2001, pp. 31-6. [21] 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. 462-4 | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/9593 | - |
| dc.description.abstract | 隨著多媒體影像技術的蓬勃發展，多媒體資料量快速地遽增，如何從龐大的多媒體資料中找到有意義的資訊和特性已成為熱門的研究議題。我們可以將影片中發生的一個事件視為一個連續的點集合，而找到影片資料庫中由點集合所構成的封閉性樣式，則可以表達出影片中發生該事件的特性。因此，在本論文中，我們提出一個有效率的探勘演算法「CVP」來探勘出影片資料庫中的封閉性樣式。我們所提出的演算法主要先利用兩種資料結構儲存頻繁樣式的資訊，以深度優先搜尋的方式先對空間維度、再對時間維度產生出可能的頻繁樣式，最後再利用我們所提出的方法來修剪不符合或不必要的樣式以及判斷其封閉性。我們的演算法利用投影資料庫去產生可能的樣式和進行修剪，並不需要重複地搜尋整個影片資料庫，因此效率能夠得到明顯的改善。在人造及真實資料庫的實驗結果中顯示我們所提出的方法較改良式Apriori的方法來得更有效率。 | zh_TW |
| dc.description.abstract | Nowadays, the number of multimedia datasets is increasing rapidly. Thus, mining implicit and meaningful patterns from multimedia databases has attracted more and more attention in recent years. The event object can be viewed as a sequence of pointsets in a video. Mining closed patterns in pointset video databases can help us understand the pattern of an event in video databases. In this thesis, we first devise two data structures, called rplist and CV-tree, to store the information of frequent video patterns. Next, we propose a novel algorithm, called CVP, to mine frequent closed patterns from a video database in a depth-first search (DFS) manner. Our proposed algorithm consists of two phases. We first grow frequent video patterns in the spatial dimension and then grow them in the temporal dimension. To efficiently mine frequent closed patterns, we develop several pruning strategies to prune non-closed patterns. The CVP algorithm can localize the candidate generation, pattern join, and support counting in a small amount of rplists. Therefore, it can efficiently mine frequent closed patterns in a video database. The experiment results show that our proposed method outperforms modified Apriori algorithm in synthetic data and real data. | en |
| dc.description.provenance | Made available in DSpace on 2021-05-20T20:30:24Z (GMT). No. of bitstreams: 1 ntu-97-R95725008-1.pdf: 504338 bytes, checksum: eb7fe6cbcb027d09ca28f6ea2bbdef17 (MD5) Previous issue date: 2008 | en |
| dc.description.tableofcontents | Table of Contents i
List of Figures ii List of Tables iii Chapter 1 Introduction 4 Chapter 2 Preliminaries and Problem Definitions 8 Chapter 3 Our Proposed Method 12 3.1 Rplist and CV-tree 12 3.2 Pattern generation 14 3.3 Closure checking and pruning strategies 21 3.4 The CVP algorithm 25 3.5 An example 29 Chapter 4 Performance Evaluation 33 4.1 Synthetic datasets 34 4.2 Performance evaluation on synthetic datasets 34 4.3 Performance evaluation on real datasets 38 Chapter 5 Conclusions and Future work 41 References 42 | |
| dc.language.iso | en | |
| dc.title | 點集合影片資料庫中封閉性樣式之資料探勘 | zh_TW |
| dc.title | Mining Closed Patterns in Pointset Video Databases | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 96-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 陳良華,沈錳坤 | |
| dc.subject.keyword | 資料探勘,點集合影片資料庫,封閉性樣式,頻繁樣式, | zh_TW |
| dc.subject.keyword | data mining,pointset video database,closed pattern,frequent pattern, | en |
| dc.relation.page | 44 | |
| dc.rights.note | 同意授權(全球公開) | |
| dc.date.accepted | 2008-08-01 | |
| 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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