{"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/skopus-mining-top-k-sequential-patterns-under","title":"Skopus: Mining top-k sequential patterns under leverage","arxiv_id":"1506.08009","date":"2015-06-26","proceeding":null,"authors":["Francois Petitjean","Tao Li","Nikolaj Tatti","Geoffrey I. Webb"],"abstract":"This paper presents a framework for exact discovery of the top-k sequential\npatterns under Leverage. It combines (1) a novel definition of the expected\nsupport for a sequential pattern - a concept on which most interestingness\nmeasures directly rely - with (2) SkOPUS: a new branch-and-bound algorithm for\nthe exact discovery of top-k sequential patterns under a given measure of\ninterest. Our interestingness measure employs the partition approach. A pattern\nis interesting to the extent that it is more frequent than can be explained by\nassuming independence between any of the pairs of patterns from which it can be\ncomposed. The larger the support compared to the expectation under\nindependence, the more interesting is the pattern. We build on these two\nelements to exactly extract the k sequential patterns with highest leverage,\nconsistent with our definition of expected support. We conduct experiments on\nboth synthetic data with known patterns and real-world datasets; both\nexperiments confirm the consistency and relevance of our approach with regard\nto the state of the art. This article was published in Data Mining and\nKnowledge Discovery and is accessible at\nhttp://dx.doi.org/10.1007/s10618-016-0467-9.","url_abs":"http://arxiv.org/abs/1506.08009v4","url_pdf":"http://arxiv.org/pdf/1506.08009v4.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":"skopus-mining-top-k-sequential-patterns-under","repo_url":"https://github.com/fpetitjean/Skopus","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}