Abstract
Finding hidden temporal structures from event sequences is a difficult task, particularly when events occur irregularly over time and temporal dependencies may exist in a long time horizon. The tasks involved are not only to find event patterns represented in the form of temporal orders, but more importantly to find patterns that are described with precise time conditions and rules that can be applied to predict when a future event will occur. Recent study has shown that a new approach based on learning temporal regions is a good solution for this problem. This paper investigates this approach in a greater depth and makes several improvements. It introduces multiple rule selection methods to better uncover hidden relations. It also introduces heuristic rule pruning methods to speed up search to solve large-scale problems. Experimental results are presented which show the effectiveness of the new methods.
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References
Agrawal and Srikant1995. R. Agrawal and R. Srikant. Mining sequential patterns. In Proceedings of the Eleventh International Conference on Data Engineering. IEEE Press, 1995. 446
Agrawal et al. 1996. R. Agrawal, H. Mannila, R. Srikant, H. Toivonen, and A. I. Verkamo. Fast discovery of association rules. In U. M. Fayyad, G. Piatetsky-Shapiro, P. Smyth, and R. Uthrumsamy, editors, Advances in Knowledge Discovery and Data Mining, chapter 12, pages 307–328. AAAI/MIT, 1996. 447
Howe and Somlo1997. A. Howe and G. Somlo. Modeling discrete event sequences as state transition diagrams. In Proceedings of the Second Conference on Intelligent Data Analysis, 1997. 446
Mannila et al.1995. H. Mannila, H. Toivonen, and A. I. Verkamo. Discovering frequent episode in sequences. In Proceedings of the First International Conference of Knowledge Discovery in Databases and Data Mining. AAAI Press, 1995. 446
Oates et al.1997. T. Oates, M. D. Schmill, D. Jensen, and P. R. Cohen. A family of algorithms for finding temporal structure in data. In Proceedings of the Sixth Internationl Workshop on Artificial Intellegence and Statistics, 1997. 446
Srikant and Agrawal1996. R. Srikant and R. Agrawal. Mining sequential patterns: generalizations and performance improvements. In Proceedings of the Fifth International Conference on Extending Database Technology, 1996. 446
Zhang1999. W. Zhang. A region-based learning approach to discovering temporal structures in data. In Proceedings of the Sixteenth International Conference on Machine Learning. Morgan Kaufmann, 1999. 446, 449, 452
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Zhang, W. (2000). Some Improvements on Event-Sequence Temporal Region Methods. In: López de Mántaras, R., Plaza, E. (eds) Machine Learning: ECML 2000. ECML 2000. Lecture Notes in Computer Science(), vol 1810. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45164-1_45
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DOI: https://doi.org/10.1007/3-540-45164-1_45
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