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Keeping it Short and Simple
2016
Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD '16
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
doi:10.1145/2939672.2939761
dblp:conf/kdd/BertensVS16
fatcat:hspq7pcsrfggxjfznti5jeahle