{"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/an-efficient-k-means-type-algorithm-for","title":"An efficient $k$-means-type algorithm for clustering datasets with incomplete records","arxiv_id":"1802.08363","date":"2018-02-23","proceeding":null,"authors":["Andrew Lithio","Ranjan Maitra"],"abstract":"The $k$-means algorithm is arguably the most popular nonparametric clustering\nmethod but cannot generally be applied to datasets with incomplete records. The\nusual practice then is to either impute missing values under an assumed\nmissing-completely-at-random mechanism or to ignore the incomplete records, and\napply the algorithm on the resulting dataset. We develop an efficient version\nof the $k$-means algorithm that allows for clustering in the presence of\nincomplete records. Our extension is called $k_m$-means and reduces to the\n$k$-means algorithm when all records are complete. We also provide\ninitialization strategies for our algorithm and methods to estimate the number\nof groups in the dataset. Illustrations and simulations demonstrate the\nefficacy of our approach in a variety of settings and patterns of missing data.\nOur methods are also applied to the analysis of activation images obtained from\na functional Magnetic Resonance Imaging experiment.","url_abs":"http://arxiv.org/abs/1802.08363v2","url_pdf":"http://arxiv.org/pdf/1802.08363v2.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":"an-efficient-k-means-type-algorithm-for","repo_url":"https://github.com/maitra/km-means","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"missing-values","task_name":"Missing Values"},{"task_slug":"nonparametric-clustering","task_name":"Nonparametric 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}