請用此 Handle URI 來引用此文件：
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45963完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李瑞庭 | |
| dc.contributor.author | Wei-Cheng Lee | en |
| dc.contributor.author | 李偉誠 | zh_TW |
| dc.date.accessioned | 2021-06-15T04:49:57Z | - |
| dc.date.available | 2013-08-06 | |
| dc.date.copyright | 2010-08-06 | |
| dc.date.issued | 2010 | |
| dc.date.submitted | 2010-08-03 | |
| dc.identifier.citation | [1] R. Agrawal, R. Srikant, Fast algorithms for mining association rules, Proceedings of the International Conference on Very Large Data Bases, Santiago, Chile, 1994, pp. 487-499.
[2] R. Agrawal, R. Srikant, Mining sequential patterns, Proceedings of the International Conference on Data Engineering, Taipei, Taiwan, 1995, pp. 3-14. [3] J. F. Allen, Maintaining knowledge about temporal intervals, Communications of the ACM, Vol. 26, No. 11, 1983, pp. 832-843. [4] S. de Amo, W.P. Junior, A. Giacometti, MILPRIT*: A constraint-based algorithm for mining temporal relational patterns, International Journal of Data Warehousing and Mining, Vol. 4, No. 4, 2008, pp. 42-61. [5] T. Guyet, R. Quiniou, Mining temporal patterns with quantitative intervals, Proceedings of International Conference on Data Mining, 2008, pp. 218-227. [6] J. Han, J. Pei, Y. Yin, Mining frequent patterns without candidate generation, Proceedings of the ACM SIGMOD International Conference on Management of Data, Dallas, USA, 2000, pp. 1-12. [7] 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, Boston, USA, 2000, pp. 355-359. [8] R. Hong, Mining frequent patterns in image and video databases, Doctoral dissertation, Department of Information Management, National Taiwan University, Taiwan, 2009. [9] F. Höppner, Learning temporal rules from state sequences, Proceedings of International Joint Conferences on Artificial Intelligence Workshop on Learning from Temporal and Spatial Data, Seattle, USA, 2001, pp. 25-31. [10] P.-S. Kam, A. W.-C. Fu, Discovering temporal patterns for interval-based events, Proceeding of Second International Conference on Data Warehousing and Knowledge Discovery, London, UK, 2000, pp. 317-326. [11] X. Kong, Q. Wei, G. Chen, An approach to discovering multi-temporal patterns and its application to financial databases, Information Sciences, Vol. 180, No. 6, 2010, pp. 873-885. [12] Y. J. Lee, J. W. Lee, D. J. Chai, B. H. Hwang, and K. H. Ryu, Mining temporal interval relational rules from temporal data, The Journal of Systems and Software, Vol. 82, No. 1, 2009, pp. 155-167. [13] P. Papapetrou, G. Kollios, S. Sclaroff, D. Gunopulos, Mining frequent arrangements of temporal intervals, Knowledge and Information Systems, Vol. 21, No. 2, 2009, pp. 133-171. [14] D. Patel, W. Hsu, M. Lee, Mining relationships among interval-based events for classification, Proceedings of the ACM SIGMOD International Conference on Management of Data, Vancouver, Canada, 2008, pp. 393-404. [15] J. Pei, J. Han, 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. [16] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, PrefixSpan: Mining sequential patterns efficiently by prefix-projected pattern growth, Proceedings of the 17th International Conference on Data Engineering, Heidelberg, Germany, 2001, pp. 215-224. [17] J. Wang, J. Han, 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. [18] J. Wang, J. Han, C. Li, Frequent closed sequence mining without candidate maintenance, IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 8, 2007, pp. 1042-1056. [19] E. Winarko, J. F. Roddick, ARMADA - An algorithm for discovering richer relative temporal association rules from interval-based data, Data and Knowledge Engineering, Vol. 63, No. 1, 2007, pp. 76-90. [20] S. Wu, Y. Chen, Mining nonambiguous temporal patterns for interval-based events. IEEE Transactions on Knowledge and Data Engineering, Vol. 19, No. 6, 2007, pp. 742-758. [21] S. Wu, Y. Chen, Discovering hybrid temporal patterns from sequences consisting of point- and interval-based events, Data and Knowledge Engineering, Vol 68, No. 11, 2009, pp. 1309-1330. [22] M.J. Zaki, SPADE: An efficient algorithm for mining frequent sequences, Machine Learning, Vol. 42, No. 1-2, 2001, pp. 31-60. [23] M.J. Zaki, 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. | |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/45963 | - |
| dc.description.abstract | 目前，已有許多學者提出探勘頻繁一維區間樣式的方法。但是，在實務上，有許多的資料是多維度的區間，如醫學療程分析中的收縮壓、舒張壓、脈博等等。因此，在本篇論文中，我們提出一個名為「MIAMI」的演算法，以頻繁樣式樹的方式依序列舉出所有的頻繁樣式，並以深度優先法遞迴產生所有的封閉性多維區間樣式。在探勘的過程中，我們設計數個有效的修剪策略以刪除不可能的樣式，以及使用封閉性測試移除非封閉性樣式。實驗結果顯示，MIAMI演算法比改良式Apriori演算法更有效率，也更具擴充性。 | zh_TW |
| dc.description.abstract | Many methods have been proposed to find frequent one-dimensional (1-D) interval patterns, where each event in the database is realized by a 1-D interval. However, the events in many applications are in nature realized by multi-dimensional intervals, such as systolic pressure, diastolic pressure, and pulse in medical treatment analysis, where each index during a certain period of time may be represented by a 1-D interval. Therefore, in this thesis, we propose an efficient algorithm, called MIAMI, to mine closed multi-dimensional interval patterns from a database. The MIAMI algorithm employs a pattern tree to enumerate all frequent patterns and generates the patterns in a depth-first search manner. In the mining process, we employ several effective pruning strategies to remove impossible patterns and perform a closure checking scheme to eliminate non-closed patterns. The experimental results show that the MIAMI algorithm is more efficient and scalable than the modified Apriori algorithm. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-15T04:49:57Z (GMT). No. of bitstreams: 1 ntu-99-R97725019-1.pdf: 498030 bytes, checksum: 8b01fb8a16c2d942278e06f801c14817 (MD5) Previous issue date: 2010 | en |
| dc.description.tableofcontents | Chapter 1 Introduction 1
Chapter 2 Preliminary Concepts and Problem Definitions 5 Chapter 3 The Proposed Method 8 3.1 Frequent pattern enumeration 8 3.1.1 Frequent pattern tree 8 3.1.2 Frequent pattern generation 9 3.2 Pruning strategies and closure checking 10 3.2.1 Pruning strategies 10 3.2.2 Positional inference matrix 10 3.2.3 Relationships support checking 11 3.2.4 Closure checking 11 3.3 The MIAMI algorithm 12 3.4 An example 16 Chapter 4 Performance Evaluation 18 4.1 Synthetic data 18 4.2 Performance evaluation on synthetic data 18 4.3 Performance evaluation on real data 23 Chapter 5 Conclusions and Future Work 26 References 27 | |
| dc.language.iso | en | |
| dc.subject | 資料探勘 | zh_TW |
| dc.subject | 多維區間樣式 | zh_TW |
| dc.subject | 一維區間樣式 | zh_TW |
| dc.subject | 頻繁樣式 | zh_TW |
| dc.subject | 封閉性樣式 | zh_TW |
| dc.subject | frequent pattern | en |
| dc.subject | data mining | en |
| dc.subject | closed pattern | en |
| dc.subject | multi-dimension interval pattern | en |
| dc.subject | 1-dimension interval pattern | en |
| dc.title | 封閉性多維區間樣式之資料探勘 | zh_TW |
| dc.title | Mining Closed Multi-Dimensional Interval Patterns | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 98-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 劉敦仁,呂永和 | |
| dc.subject.keyword | 多維區間樣式,一維區間樣式,頻繁樣式,封閉性樣式,資料探勘, | zh_TW |
| dc.subject.keyword | multi-dimension interval pattern,1-dimension interval pattern,frequent pattern,closed pattern,data mining, | en |
| dc.relation.page | 30 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2010-08-03 | |
| 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 | |
文件中的檔案：
| 檔案 | 大小 | 格式 | |
|---|---|---|---|
| ntu-99-1.pdf 未授權公開取用 | 486.36 kB | Adobe PDF |
系統中的文件，除了特別指名其著作權條款之外，均受到著作權保護，並且保留所有的權利。