{"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-simple-approach-to-sparse-clustering","title":"A Simple Approach to Sparse Clustering","arxiv_id":"1602.07277","date":"2016-02-23","proceeding":null,"authors":["Ery Arias-Castro","Xiao Pu"],"abstract":"Consider the problem of sparse clustering, where it is assumed that only a\nsubset of the features are useful for clustering purposes. In the framework of\nthe COSA method of Friedman and Meulman, subsequently improved in the form of\nthe Sparse K-means method of Witten and Tibshirani, a natural and simpler\nhill-climbing approach is introduced. The new method is shown to be competitive\nwith these two methods and others.","url_abs":"http://arxiv.org/abs/1602.07277v2","url_pdf":"http://arxiv.org/pdf/1602.07277v2.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-simple-approach-to-sparse-clustering","repo_url":"https://github.com/victorpu/SAS_Hill_Climb","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}