{"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/multi-view-banded-spectral-clustering-with","title":"Multi-view Banded Spectral Clustering with Application to ICD9 Clustering","arxiv_id":"1804.02097","date":"2018-04-06","proceeding":null,"authors":["Luwan Zhang","Katherine Liao","Issac Kohane","Tianxi Cai"],"abstract":"Despite recent development in methodology, community detection remains a\nchallenging problem. Existing literature largely focuses on the standard\nsetting where a network is learned using an observed adjacency matrix from a\nsingle data source. Constructing a shared network from multiple data sources is\nmore challenging due to the heterogeneity across populations. Additionally, no\nexisting method leverages the prior distance knowledge available in many\ndomains to help the discovery of the network structure. To bridge this gap, in\nthis paper we propose a novel spectral clustering method that optimally\ncombines multiple data sources while leveraging the prior distance knowledge.\nThe proposed method combines a banding step guided by the distance knowledge\nwith a subsequent weighting step to maximize consensus across multiple sources.\nIts statistical performance is thoroughly studied under a multi-view stochastic\nblock model. We also provide a simple yet optimal rule of choosing weights in\npractice. The efficacy and robustness of the method is fully demonstrated\nthrough extensive simulations. Finally, we apply the method to cluster the\nInternational classification of diseases, ninth revision (ICD9), codes and\nyield a very insightful clustering structure by integrating information from a\nlarge claim database and two healthcare systems.","url_abs":"http://arxiv.org/abs/1804.02097v2","url_pdf":"http://arxiv.org/pdf/1804.02097v2.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":"multi-view-banded-spectral-clustering-with","repo_url":"https://github.com/celehs/mvBSC","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"community-detection","task_name":"Community Detection"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[{"method_slug":"spectral-clustering","method_name":"Spectral Clustering"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}