從序列式資料庫挖掘多標籤時間序列樣式

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姓名

王昱程(Yu-Cheng Wang) 查詢紙本館藏

畢業系所

資訊管理學系

論文名稱

從序列式資料庫挖掘多標籤時間序列樣式
(Discovering Multi-label Temporal Patterns in Sequence Database)

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摘要(中)

序列樣式探勘是在知識發現和資料挖掘的領域中很重要的一樣技術之一。過去已有很多學者針對序列樣式探勘提出了很多延伸的方法在各種日常應用領域中。以往的研究集中於點式事件和區間式事件或混合事件（包括點式和區間式事件）。然而，在很多日常應用中，事件可能有很多的狀態，不只是發生在某一個時間點或在某一段時間。本研究提出了一個一般化的表達方式來表達時間事件。我們將這些事件視為多標籤的事件，用來代表事件的不同狀態，並提出了MLTPM演算法來從資料庫中挖掘多標籤的時間序列樣式。由於MLTPM是過去方法的一般化，因此它也可以用來處理點式事件，區間式事件和混合事件。實驗結果顯示，MLTPM演算法的效能是可以接受的，且可以找出許多過去方法所忽略的樣式。

摘要(英)

Sequence pattern mining is one of the most important techniques in knowledge discovery and data mining domain. There were many researches extended the problem of sequential pattern mining in various daily applications. Previous research focused on the point-based event and interval-based event or hybrid event (including point-based and interval-based event). However, in many real life applications, events may have many statuses, not just happens at a certain time point or over a period of time. In this work, we proposed a generalized representation of temporal events. We treated these events as multi-label events which have many statuses, and introduced an algorithm called MLTPM to discover multi-label temporal patterns from temporal database. Since MLTPM is a generalized model of previous methods, it can also deal with point-based events, interval-based events, and hybrid events. The experimental result showed that the MLTPM’s performance is acceptable and can discover interesting patterns.

關鍵字(中)

★ 區間式事件序列
★ 點式事件序列
★ 多標籤時間序列樣式
★ 時間區間樣式
★ 序列樣式

關鍵字(英)

★ interval-based event sequence
★ point-based event sequence
★ temporal patterns
★ sequential patterns
★ multi-label temporal patterns

論文目次

摘要
ABSTRACT
LIST OF FIGURES
LIST OF TABLES
CHAPTER 1 INTRODUCTION
CHAPTER 2 RELATIVE WORKS
2.1 PREVIOUS WORK OF SEQUENTIAL PATTERN MINING
2.2 WHY TRADITIONAL MODELS CAN NOT BE USED TO PRESENT MULTI-LABEL EVENTS
CHAPTER 3 PROBLEM DEFINITION
CHAPTER 4 ALGORITHMS
4.1 PHASE 1 (INTRA-PATTERN MINING)
4.2 PHASE 2 (INTER-PATTERN MINING)
CHAPTER 5 EXPERIMENTS
5.1 PERFORMANCE EVALUATION
5.1.1. DISCOVERING PATTERNS FROM POINT-BASED EVENT SEQUENCES
5.1.2. DISCOVERING PATTERNS FROM MULTI-LABEL EVENT SEQUENCES
5.2 NUMBER OF PATTERNS’ COMPARISONS
5.2.1. DISCOVERING PATTERNS FROM INTERVAL-BASED EVENT SEQUENCES
5.2.2. DISCOVERING PATTERNS FROM HYBRID EVENT SEQUENCES
5.3 REAL CASE ANALYSIS
5.3.1 DATA PRE-PROCESSING
5.3.2 DISCOVERING MULTI-LABEL TEMPORAL PATTERNS FROM STOCK DATA
5.3.3 PREDICTIVE ACCURACY
CHAPTER 6 CONCLUSION AND FUTURE WORKS
REFERENCE
APPENDIXES
APPENDIX A. THE MINING RESULT FROM TEMPORAL DATABASE D

參考文獻

[1] S. Achelis, Technical analysis from A to Z: New York : McGraw Hill, 2001.
[2] R. Agrawal, C. Faloutsos, and A. Swami, “Efficient Similarity Search In Sequence Databases,” Proceedings of the 4th International Conference of Foundations of Data Organization and Algorithms (FODO), pp. 69-84, 1993.
[3] R. Agrawal, and R. Srikant, “Mining sequential patterns,” Eleventh International Conference on Data Engineering, pp. 3-14, 1995.
[4] Ajax (programming), wikipedia. http://en.wikipedia.org/wiki/AJAX
[5] J. Allen, “Maintaining knowledge about temporal intervals,” Communications of ACM, vol. 26, no. 11, pp. 832-843, 1983.
[6] C. M. Antunes, and A. L. Oliveira, “Temporal data mining: An overview,” KDD 2001 Workshop on Temporal Data Mining, 2001.
[7] M. Chen, J. Park, and P. Yu, “Efficient Data Mining for Path Traversal Patterns,” Knowledge and Data Engineering, vol. 10, no. 2, pp. 209-221, 1998.
[8] Y. L. Chen, M. C. Chiang, and M. T. Kao, “Discovering time-interval sequential patterns in sequence databases,” Expert Systems with Applications, vol. 25, pp. 343-354, 2003.
[9] Y.-L. Chen, and T. C. K. Huang, “Discovering fuzzy time-interval sequential patterns in sequence databases,” IEEE Trans on Systems, Man, Cybernetics- Part B, vol. 35, no. 5, pp. 959-972, 2005.
[10] R. Cooley, B. Mobasher, and J. Srivastava, “Data Preparation for Mining World Wide Web Browsing Patterns,” Knowledge and Information Systems, vol. 1, no. 1, pp. 5-32, 1999.
[11] J. Han, J. Pei, B. Mortazavi-Asl et al., “FreeSpan: frequent pattern-projected sequential pattern mining,” KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 355-359, 2000.
[12] F. Hoppner, and F. Klawonn, “Finding Informative Rules in Interval Sequences,” Lecture Notes in Computer Science, vol. 2189, 2001.
[13] Jesse James Garret, Ajax: A New Approach to Web Applications. http://www.adaptivepath.com/publications/essays/archives/000385.php
[14] P. Kam, and Ada, “Discovering Temporal Patterns for Interval-Based Events,” Second International Conference on Data Warehousing and Knowledge Discovery (DaWaK 2000), vol. 1874, pp. 317-326, 2000.
[15] N. Lesh, M. Zaki, and M. Ogihara, “Mining Features for Sequence Classification,” Fifth ACM {SIGKDD} International Conference on Knowledge Discovery and Data Mining, 1999.
[16] C.-S. Li, P. S. Yu, and V. Castelli, “HierarchyScan: a hierarchical similarity search algorithm for databases of long sequences,” Proceedings of the Twelfth International Conference on Data Engineering, pp. 546-553, 1996.
[17] H. Mannila, H. Toivonen, and I. Verkamo, “Discovery of Frequent Episodes in Event Sequences,” Data Mining and Knowledge Discovery, vol. 1, no. 3, pp. 259-289, 1997.
[18] J. Pei, J. Han, M. Asl et al., “PrefixSpan: Mining Sequential Patterns Efficiently by Prefix Projected Pattern Growth,” Proc.17th Int’l Conf. on Data Eng., pp. 215-226, 2001.
[19] J. Pei, and J. Han, “Constrained frequent pattern mining: a pattern-growth view,” SIGKDD Explor. Newsl., vol. 4, no. 1, pp. 31-39, 2002.
[20] R. Srikant, and R. Agrawal, “Mining Sequential Patterns: Generalizations and Performance Improvements,” Proc. 5th Int. Conf. Extending Database Technology (EDBT), vol. 1057, pp. 3-17, 1996.
[21] A. Tansel, and N. Ayan, “Discovery of Association Rules in Temporal Databases,” Proc. of AAAI on Knowledge Discovery in Databases, 1998.
[22] Tr, H. Kum, J. Pei et al., “ApproxMAP: Approximate Mining of Consensus Sequential Patterns,” Proceedings of the 3rd SIAM International Conference on Data Mining, pp. 311-315, 2002.
[23] R. Villafane, K. Hua, D. Tran et al., “Mining Interval Time Series,” DaWaK '99: Proceedings of the First International Conference on Data Warehousing and Knowledge Discovery, pp. 318-330, 1999.
[24] M. Wojciechowski, T. Morzy, and M. Zakrzewicz, “Efficient Constraint-Based Sequential Pattern Mining Using Dataset Filtering Techniques,” Proc. Fifth IEEE Int’l Baltic Workshop on Databases & Information Systems (DB&IS "02), pp. 213-224, 2002.
[25] S.-Y. Wu, and Y.-L. Chen, “Mining Non-ambiguous Temporal Patterns for Interval-Based Events,” IEEE Transactions on Knowledge and Data Engineering, vol. 19, no. 6, pp. 742-758, 2007.
[26] S.-Y. Wu, and Y.-L. Chen, “Discovering Hybrid Temporal Patterns from Sequences Consisting of Point- and Interval-Based Events,” Submitted to Data and Knowledge Engineering.
[27] C.-C. Yu, and Y.-L. Chen, “Mining sequential patterns from multidimensional sequence data,” IEEE Transactions on Knowledge and Data Engineering, vol. 17, no. 1, pp. 136-140, 2005.
[28] M. Zaki, “SPADE: An Efficient Algorithm for Mining Frequent Sequences,” Machine Learning, vol. 42, no. 1/2, pp. 31-60, 2001.

指導教授

陳彥良(Yen-Liang Chen)

審核日期

2008-7-7

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