We study how to obtain concise descriptions of discrete multivariate
sequential data. In particular, how to do so in terms of rich multivariate
sequential patterns that can capture potentially highly interesting
(cor)relations between sequences...
To this end we allow our pattern language to
span over the domains (alphabets) of all sequences, allow patterns to overlap
temporally, as well as allow for gaps in their occurrences. We formalise our goal by the Minimum Description Length principle, by which
our objective is to discover the set of patterns that provides the most
succinct description of the data. To discover high-quality pattern sets
directly from data, we introduce DITTO, a highly efficient algorithm that
approximates the ideal result very well. Experiments show that DITTO correctly discovers the patterns planted in
synthetic data. Moreover, it scales favourably with the length of the data, the
number of attributes, the alphabet sizes. On real data, ranging from sensor
networks to annotated text, DITTO discovers easily interpretable summaries that
provide clear insight in both the univariate and multivariate structure.