Frequent Temporal Pattern Mining with Extended Lists
In this paper we consider Temporal Pattern Mining (TPM) for extracting predictive class-specific patterns from multivariate time series. We suggest a new approach that extends usage of the a priori property which requires a more complex pattern to appear only at places where all its subpatterns appear as well. It is based on tracking positions of a pattern inside records in a greedy manner. We demonstrate that it outperforms the previous version of the TMP on several real-life data sets independent of the way how the temporal pattern is defined.
Research was supported by RSF grant 14-41-00039.
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