Abstract
One basic goal in the analysis of time-series data is to find frequent interesting episodes, i.e, collections of events occurring frequently together in the input sequence. Most widely-known work decide the interestingness of an episode from a fixed user-specified window width or interval, that bounds the length of the subsequent sequential association rules. We present in this paper, a more intuitive definition that allows, in turn, interesting episodes to grow during the mining without any user-specified help. A convenient algorithm to efficiently discover the proposed unbounded episodes is also implemented. Experimental results confirm that our approach results useful and advantageous.
This work is supported in part by EU ESPRIT IST-1999-14186 (ALCOM-FT), and MCYT TIC 2002-04019-C03-01 (MOISES)
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Casas-Garriga, G. (2003). Discovering Unbounded Episodes in Sequential Data. In: Lavrač, N., Gamberger, D., Todorovski, L., Blockeel, H. (eds) Knowledge Discovery in Databases: PKDD 2003. PKDD 2003. Lecture Notes in Computer Science(), vol 2838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39804-2_10
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DOI: https://doi.org/10.1007/978-3-540-39804-2_10
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