Srivatsan Laxman, P. S. Sastry, and K. P. Unnikrishnan
Discovering patterns in temporal data is an important task in Data Mining. A successful method for this was proposed by Mannila et al.  in 1997. In their framework, mining for temporal patterns in a database of sequences of events is done by discovering the so called frequent episodes. These episodes characterize interesting collections of events occurring relatively close to each other in some partial order. However, in this framework(and in many others for finding patterns in event sequences), the ordering of events in an event sequence is the only allowed temporal information. But there are many applications where the events are not instantaneous; they have time durations. Interesting episodesthat we want to discover may need to contain information regarding event durations etc. In this paper we extend Mannila et al.’s framework to tackle such issues. In our generalized formulation, episodes are defined so that much more temporal information about events can be incorporated into the structure of an episode. This significantly enhances the expressive capability of the rules that can be discovered in the frequent episode framework. We also present algorithms for discovering such generalized frequent episodes.
In Temporal Data Mining Workshop Notes, SIGKDD, Edmonton, Alberta, Canada
Publisher Association for Computing Machinery, Inc.
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