Abstract
Recently, association rule mining has been generalized to the discovery of episodes in event sequences. In this paper, we additionally take durations into account and thus present a generalization to time intervals. We discover frequent temporal patterns in a single series of such labeled intervals, which we call a state sequence. A temporal pattern is denned as a set of states together with their interval relationships described in terms of Allen’s interval logic, for instance “A before B, A overlaps C, C overlaps B” or equivalently “state A ends before state B starts, the gap is covered by state C”. As an example we consider the problem of deriving local weather forecasting rules that allow us to conclude from the qualitative behaviour of the air-pressure curve to the wind-strength. Here, the states have been extracted automatically from (multivariate) time series and characterize the trend of the time series locally within the assigned time interval.
This work has been supported by the Deutsche Forschungsgemeinschaft (DFG) under grant no. K1 648/1.
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Höppner, F. (2001). Discovery of Temporal Patterns. In: De Raedt, L., Siebes, A. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2001. Lecture Notes in Computer Science(), vol 2168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44794-6_16
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DOI: https://doi.org/10.1007/3-540-44794-6_16
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