  In this paper we address the problem of discovering a small set of frequent
serial episodes from sequential data so as to adequately characterize or
summarize the data. We discuss an algorithm based on the Minimum Description
Length (MDL) principle and the algorithm is a slight modification of an earlier
method, called CSC-2. We present a novel generative model for sequence data
containing prominent pairs of serial episodes and, using this, provide some
statistical justification for the algorithm. We believe this is the first
instance of such a statistical justification for an MDL based algorithm for
summarizing event sequence data. We then present a novel application of this
data mining algorithm in text classification. By considering text documents as
temporal sequences of words, the data mining algorithm can find a set of
characteristic episodes for all the training data as a whole. The words that
are part of these characteristic episodes could then be considered the only
relevant words for the dictionary thus resulting in a considerably reduced
feature vector dimension. We show, through simulation experiments using
benchmark data sets, that the discovered frequent episodes can be used to
achieve more than four-fold reduction in dictionary size without losing any
classification accuracy.
