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Recent sequential pattern mining methods have used the minimum description length (MDL) principle to define an encoding scheme which describes an algorithm for mining the most compressing patterns in a database. We present a novel subsequence interleaving model based on a probabilistic model of the sequence database, which allows us to searchfor the most compressing set of patterns without designing a specific encoding scheme. Our proposed algorithm is able to efficiently mine the most relevant sequential patterns and rank them using an associated measure of interestingness.The efficient inference in our model is a direct result of our use of a structural expectation-maximization framework, in which the expectation-step takes the form of a submodular optimization problem subject to a coverage constraint.We show on both synthetic and real world datasets that ourmodel mines a set of sequential patterns with low spuriousness and redundancy, high interpretability and usefulness in real-world applications. Furthermore, we demonstrate that the quality of the patterns from our approach is comparable to, if not better than, existing state of the art sequential pattern mining algorithms.
|Title of host publication||KDD '16 Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Place of Publication||San Francisco , United States|
|Number of pages||10|
|Publication status||Published - 13 Aug 2016|
|Event||22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - San Francisco , United States|
Duration: 13 Aug 2016 → 17 Aug 2016
|Conference||22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining|
|Period||13/08/16 → 17/08/16|
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