{"type":"ScholarlyArticle","title":"An Information Theory based Approach to Multisource Clustering","authors":[{"name":"Pierre-Alexandre Murena","slug":"pierre-alexandre-murena","given":"Pierre-Alexandre","family":"Murena","affiliation":"LTCI -Télécom ParisTech , Paris , France; UMR MIA-Paris , AgroParisTech , INRA , Université Paris-Saclay , Paris , France"},{"name":"Jérémie Sublime","slug":"jeremie-sublime","given":"Jérémie","family":"Sublime","affiliation":"LISITE laboratory -RDI team , ISEP , 10 rue de Vanves , Issy-les-Moulineaux , France; Université Paris 13 - Sorbonne Paris Cité , LIPN -CNRS UMR 7030 , Villetaneuse , France"},{"name":"Basarab Matei","slug":"basarab-matei","given":"Basarab","family":"Matei","affiliation":"Université Paris 13 - Sorbonne Paris Cité , LIPN -CNRS UMR 7030 , Villetaneuse , France"},{"name":"Antoine Cornuéjols","slug":"antoine-cornuejols","given":"Antoine","family":"Cornuéjols","affiliation":"UMR MIA-Paris , AgroParisTech , INRA , Université Paris-Saclay , Paris , France"}],"abstract":"Clustering is a compression task which consists in grouping similar objects into clusters. \r\nIn real-life applications, the system may have access to several views of the same data and each view may be processed by a specific clustering algorithm: this framework is called multi-view clustering and can benefit from algorithms capable of exchanging information between the different views. \r\nIn this paper, we consider this type of unsupervised ensemble learning as a compression problem and develop a theoretical framework based on algorithmic theory of information suitable for multi-view clustering and collaborative clustering applications. \r\nUsing this approach, we propose a new algorithm based on solid theoretical basis, and test it on several real and artificial data sets.","doi":"10.24963/ijcai.2018/358","url":"https://ijcai.org/proceedings/2018/358","pdf":"https://ijcai.org/proceedings/2018/0358.pdf","markdown":"https://ijcai.org/proceedings/2018/358.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/358","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":358,"pages":{"first":2581,"last":2587},"keywords":["Machine Learning: Multi-instance;Multi-label;Multi-view learning","Machine Learning: Clustering"],"videos":[],"references":[{"position":1,"raw":"[Bickel and Scheffer, 2005] Steffen Bickel and Tobias Scheffer. 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