{"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/clustering-via-boundary-erosion","title":"Clustering via Boundary Erosion","arxiv_id":"1804.04312","date":"2018-04-12","proceeding":null,"authors":["Cheng-Hao Deng","Wan-Lei Zhao"],"abstract":"Clustering analysis identifies samples as groups based on either their mutual\ncloseness or homogeneity. In order to detect clusters in arbitrary shapes, a\nnovel and generic solution based on boundary erosion is proposed. The clusters\nare assumed to be separated by relatively sparse regions. The samples are\neroded sequentially according to their dynamic boundary densities. The erosion\nstarts from low density regions, invading inwards, until all the samples are\neroded out. By this manner, boundaries between different clusters become more\nand more apparent. It therefore offers a natural and powerful way to separate\nthe clusters when the boundaries between them are hard to be drawn at once.\nWith the sequential order of being eroded, the sequential boundary levels are\nproduced, following which the clusters in arbitrary shapes are automatically\nreconstructed. As demonstrated across various clustering tasks, it is able to\noutperform most of the state-of-the-art algorithms and its performance is\nnearly perfect in some scenarios.","url_abs":"http://arxiv.org/abs/1804.04312v2","url_pdf":"http://arxiv.org/pdf/1804.04312v2.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":"clustering-via-boundary-erosion","repo_url":"https://github.com/redfoxdch/beClustering","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"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}