{"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-bayesian-method-for-joint-clustering-of","title":"A Bayesian Method for Joint Clustering of Vectorial Data and Network Data","arxiv_id":"1710.08846","date":"2017-10-24","proceeding":null,"authors":["Yunchuan Kong","Xiaodan Fan"],"abstract":"We present a new model-based integrative method for clustering objects given\nboth vectorial data, which describes the feature of each object, and network\ndata, which indicates the similarity of connected objects. The proposed general\nmodel is able to cluster the two types of data simultaneously within one\nintegrative probabilistic model, while traditional methods can only handle one\ndata type or depend on transforming one data type to another. Bayesian\ninference of the clustering is conducted based on a Markov chain Monte Carlo\nalgorithm. A special case of the general model combining the Gaussian mixture\nmodel and the stochastic block model is extensively studied. We used both\nsynthetic data and real data to evaluate this new method and compare it with\nalternative methods. The results show that our simultaneous clustering method\nperforms much better. This improvement is due to the power of the model-based\nprobabilistic approach for efficiently integrating information.","url_abs":"http://arxiv.org/abs/1710.08846v1","url_pdf":"http://arxiv.org/pdf/1710.08846v1.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-bayesian-method-for-joint-clustering-of","repo_url":"https://github.com/yunchuankong/SharedClustering","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[{"task_slug":"bayesian-inference","task_name":"Bayesian Inference"},{"task_slug":"clustering","task_name":"Clustering"},{"task_slug":"stochastic-block-model","task_name":"Stochastic Block Model"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}