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In this paper we address the problem of discovering a small set of frequentserial episodes from sequential data so as to adequately characterize orsummarize the data. We discuss an algorithm based on the Minimum DescriptionLength (MDL) principle and the algorithm is a slight modification of an earliermethod, called CSC-2. We present a novel generative model for sequence datacontaining prominent pairs of serial episodes and, using this, provide somestatistical justification for the algorithm. We believe this is the firstinstance of such a statistical justification for an MDL based algorithm forsummarizing event sequence data. We then present a novel application of thisdata mining algorithm in text classification. By considering text documents astemporal sequences of words, the data mining algorithm can find a set ofcharacteristic episodes for all the training data as a whole. The words thatare part of these characteristic episodes could then be considered the onlyrelevant words for the dictionary thus resulting in a considerably reducedfeature vector dimension. We show, through simulation experiments usingbenchmark data sets, that the discovered frequent episodes can be used toachieve more than four-fold reduction in dictionary size without losing anyclassification accuracy.

Summarizing Event Sequences with Serial Episodes: A Statistical Model and an Application