{"type":"ScholarlyArticle","title":"Multiple Medoids based Multi-view Relational Fuzzy Clustering with Minimax Optimization","authors":[{"name":"Yangtao Wang","slug":"yangtao-wang","given":"Yangtao","family":"Wang","affiliation":"Institude for Infocomm Research(I2R) , A *STAR , Singapore"},{"name":"Lihui Chen","slug":"lihui-chen","given":"Lihui","family":"Chen","affiliation":"School of Electrical and Electronic Engineering , Nanyang Technological University , Singapore"},{"name":"Xiao-Li Li","slug":"xiao-li-li","given":"Xiao-Li","family":"Li","affiliation":"Institude for Infocomm Research(I2R) , A *STAR , Singapore"}],"abstract":"Multi-view data becomes prevalent nowadays because more and more data can be collected from various sources. Each data set may be described by different set of features, hence forms a multi-view data set or multi-view data in short. To find the underlying pattern embedded in an unlabelled multi-view data, many multi-view clustering approaches have been proposed. Fuzzy clustering in which a data object can belong to several clusters with different memberships is widely used in many applications. However, in most of the fuzzy clustering approaches, a single center or medoid is considered as the representative of each cluster in the end of clustering process. This may not be sufficient to ensure accurate data analysis. In this paper, a new multi-view fuzzy clustering approach based on multiple medoids and minimax optimization called M4-FC for relational data is proposed. In M4-FC, every object is considered as a medoid candidate with a weight.  The higher the weight is, the more likely the object is chosen as the final medoid. In the end of clustering process, there may be more than one mediod in each cluster. Moreover, minimax optimization is applied to find consensus clustering results of different views with its set of features. Extensive experimental studies on several multi-view data sets including real world image and document data sets demonstrate that M4-FC not only outperforms single medoid based multi-view fuzzy clustering approach, but also performs better than existing multi-view relational clustering approaches.","doi":"10.24963/ijcai.2017/414","url":"https://ijcai.org/proceedings/2017/414","pdf":"https://ijcai.org/proceedings/2017/0414.pdf","markdown":"https://ijcai.org/proceedings/2017/414.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/414","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":414,"pages":{"first":2971,"last":2977},"keywords":["Machine Learning: Unsupervised Learning","Machine Learning: Multi-instance/Multi-label/Multi-view learning","Machine Learning: Data Mining","Machine Learning: Machine Learning"],"videos":[],"references":[{"position":1,"raw":"[Amini et al., 2009] Massih Amini, Nicolas Usunier, and Cyril Goutte. 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