{"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/merging-k-means-with-hierarchical-clustering","title":"Merging $K$-means with hierarchical clustering for identifying general-shaped groups","arxiv_id":"1712.08786","date":"2017-12-23","proceeding":null,"authors":["Anna D. Peterson","Arka P. Ghosh","Ranjan Maitra"],"abstract":"Clustering partitions a dataset such that observations placed together in a\ngroup are similar but different from those in other groups. Hierarchical and\n$K$-means clustering are two approaches but have different strengths and\nweaknesses. For instance, hierarchical clustering identifies groups in a\ntree-like structure but suffers from computational complexity in large datasets\nwhile $K$-means clustering is efficient but designed to identify homogeneous\nspherically-shaped clusters. We present a hybrid non-parametric clustering\napproach that amalgamates the two methods to identify general-shaped clusters\nand that can be applied to larger datasets. Specifically, we first partition\nthe dataset into spherical groups using $K$-means. We next merge these groups\nusing hierarchical methods with a data-driven distance measure as a stopping\ncriterion. Our proposal has the potential to reveal groups with general shapes\nand structure in a dataset. We demonstrate good performance on several\nsimulated and real datasets.","url_abs":"http://arxiv.org/abs/1712.08786v1","url_pdf":"http://arxiv.org/pdf/1712.08786v1.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":[],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"density-estimation","task_name":"Density Estimation"},{"task_slug":"speech-synthesis","task_name":"Speech Synthesis"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/speech-synthesis-on-north-american-english","task":"Speech Synthesis","dataset":"North American English","model":"means","rank_in_archive_order":7,"of":7,"metrics":{"Mean Opinion Score":"0"},"uses_additional_data":false}],"syntology":{"syntology_url":null,"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}