{"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-new-spectral-clustering-algorithm","title":"A New Spectral Clustering Algorithm","arxiv_id":"1710.02756","date":"2017-10-07","proceeding":null,"authors":["W. R. Casper","Balu Nadiga"],"abstract":"We present a new clustering algorithm that is based on searching for natural\ngaps in the components of the lowest energy eigenvectors of the Laplacian of a\ngraph. In comparing the performance of the proposed method with a set of other\npopular methods (KMEANS, spectral-KMEANS, and an agglomerative method) in the\ncontext of the Lancichinetti-Fortunato-Radicchi (LFR) Benchmark for undirected\nweighted overlapping networks, we find that the new method outperforms the\nother spectral methods considered in certain parameter regimes. Finally, in an\napplication to climate data involving one of the most important modes of\ninterannual climate variability, the El Nino Southern Oscillation phenomenon,\nwe demonstrate the ability of the new algorithm to readily identify different\nflavors of the phenomenon.","url_abs":"http://arxiv.org/abs/1710.02756v1","url_pdf":"http://arxiv.org/pdf/1710.02756v1.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-new-spectral-clustering-algorithm","repo_url":"https://github.com/eXascaleInfolab/LFR-Benchmark_UndirWeightOvp","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}