{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/a-subsequence-interleaving-model-for","title":"A Subsequence Interleaving Model for Sequential Pattern Mining","arxiv_id":"1602.05012","date":"2016-02-16","proceeding":null,"authors":["Jaroslav Fowkes","Charles Sutton"],"abstract":"Recent sequential pattern mining methods have used the minimum description\nlength (MDL) principle to define an encoding scheme which describes an\nalgorithm for mining the most compressing patterns in a database. We present a\nnovel subsequence interleaving model based on a probabilistic model of the\nsequence database, which allows us to search for the most compressing set of\npatterns without designing a specific encoding scheme. Our proposed algorithm\nis able to efficiently mine the most relevant sequential patterns and rank them\nusing an associated measure of interestingness. The efficient inference in our\nmodel is a direct result of our use of a structural expectation-maximization\nframework, in which the expectation-step takes the form of a submodular\noptimization problem subject to a coverage constraint. We show on both\nsynthetic and real world datasets that our model mines a set of sequential\npatterns with low spuriousness and redundancy, high interpretability and\nusefulness in real-world applications. Furthermore, we demonstrate that the\nquality of the patterns from our approach is comparable to, if not better than,\nexisting state of the art sequential pattern mining algorithms.","url_abs":"http://arxiv.org/abs/1602.05012v2","url_pdf":"http://arxiv.org/pdf/1602.05012v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"a-subsequence-interleaving-model-for","repo_url":"https://github.com/mast-group/sequence-mining","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"sequential-pattern-mining","task_name":"Sequential Pattern Mining"},{"task_slug":"model","task_name":"model"}],"methods":[{"method_slug":"interpretability","method_name":"Interpretability"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}