Abstract
Discovering patterns with great significance is an important problem in data mining discipline. A serial episode is defined to be a partially ordered set of events for consecutive and fixed-time intervals in a sequence. Previous studies on serial episodes consider only frequent serial episodes in a sequence of events (called simple sequence). In real world, we may find a set of events at each time slot in terms of various intervals (called complex sequence). Mining frequent serial episodes in complex sequences has more extensive applications than that in simple sequences. In this paper, we discuss the problem on mining frequent serial episodes in a complex sequence. We extend previous algorithm MINEPI to MINEPI+ for serial episode mining from complex sequences. Furthermore, a memory-anchored algorithm called EMMA is introduced for the mining task.
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Huang, K.Y., Chang, C.H.: Smca: A general model for mining synchronous periodic pattern in temporal database. IEEE Transaction on Knowledge and Data Engineering (TKDE) 17(6), 776–785 (2005)
Luo, J., Bridges, S.M.: Mining fuzzy association rules and fuzzy frequency episodes for intrusion detection. International Journal of Intelligent Systems 15(8) (2000)
Mannila, H., Toivonen, H., Verkamo, A.I.: Discovering frequent episodes in sequences. In: Proceedings of the First International Conference on Knowledge Discovery and Data Mining (KDD 1995), pp. 210–215 (1995)
Mannila, H., Toivonen, H.: Discovering generalized episodes using minimal occurrences. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD 1996), pp. 146–151 (1996)
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© 2006 Springer-Verlag Berlin Heidelberg
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Huang, KY., Chang, CH. (2006). Efficient Mining Strategy for Frequent Serial Episodes in Temporal Database. In: Zhou, X., Li, J., Shen, H.T., Kitsuregawa, M., Zhang, Y. (eds) Frontiers of WWW Research and Development - APWeb 2006. APWeb 2006. Lecture Notes in Computer Science, vol 3841. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11610113_80
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DOI: https://doi.org/10.1007/11610113_80
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31142-3
Online ISBN: 978-3-540-32437-9
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