{"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-bayesian-network-model-for-interesting","title":"A Bayesian Network Model for Interesting Itemsets","arxiv_id":"1510.04130","date":"2015-10-14","proceeding":null,"authors":["Jaroslav Fowkes","Charles Sutton"],"abstract":"Mining itemsets that are the most interesting under a statistical model of\nthe underlying data is a commonly used and well-studied technique for\nexploratory data analysis, with the most recent interestingness models\nexhibiting state of the art performance. Continuing this highly promising line\nof work, we propose the first, to the best of our knowledge, generative model\nover itemsets, in the form of a Bayesian network, and an associated novel\nmeasure of interestingness. Our model is able to efficiently infer interesting\nitemsets directly from the transaction database using structural EM, in which\nthe E-step employs the greedy approximation to weighted set cover. Our approach\nis theoretically simple, straightforward to implement, trivially parallelizable\nand retrieves itemsets whose quality is comparable to, if not better than,\nexisting state of the art algorithms as we demonstrate on several real-world\ndatasets.","url_abs":"http://arxiv.org/abs/1510.04130v2","url_pdf":"http://arxiv.org/pdf/1510.04130v2.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-bayesian-network-model-for-interesting","repo_url":"https://github.com/mast-group/itemset-mining","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"model","task_name":"model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}