{"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/flexible-constrained-sampling-with-guarantees","title":"Flexible constrained sampling with guarantees for pattern mining","arxiv_id":"1610.09263","date":"2016-10-28","proceeding":null,"authors":["Vladimir Dzyuba","Matthijs van Leeuwen","Luc De Raedt"],"abstract":"Pattern sampling has been proposed as a potential solution to the infamous\npattern explosion. Instead of enumerating all patterns that satisfy the\nconstraints, individual patterns are sampled proportional to a given quality\nmeasure. Several sampling algorithms have been proposed, but each of them has\nits limitations when it comes to 1) flexibility in terms of quality measures\nand constraints that can be used, and/or 2) guarantees with respect to sampling\naccuracy. We therefore present Flexics, the first flexible pattern sampler that\nsupports a broad class of quality measures and constraints, while providing\nstrong guarantees regarding sampling accuracy. To achieve this, we leverage the\nperspective on pattern mining as a constraint satisfaction problem and build\nupon the latest advances in sampling solutions in SAT as well as existing\npattern mining algorithms. Furthermore, the proposed algorithm is applicable to\na variety of pattern languages, which allows us to introduce and tackle the\nnovel task of sampling sets of patterns. We introduce and empirically evaluate\ntwo variants of Flexics: 1) a generic variant that addresses the well-known\nitemset sampling task and the novel pattern set sampling task as well as a wide\nrange of expressive constraints within these tasks, and 2) a specialized\nvariant that exploits existing frequent itemset techniques to achieve\nsubstantial speed-ups. Experiments show that Flexics is both accurate and\nefficient, making it a useful tool for pattern-based data exploration.","url_abs":"http://arxiv.org/abs/1610.09263v2","url_pdf":"http://arxiv.org/pdf/1610.09263v2.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":"flexible-constrained-sampling-with-guarantees","repo_url":"https://bitbucket.org/wxd/flexics","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"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}