Soft Comput DOI 10.1007/s00500-017-2801-6 METHODOLOGIES AND APPLICATION Collaborative multi-view K-means clustering Safa Bettoumi 1 · Chiraz Jlassi 1 · Najet Arous 1 © Springer-Verlag GmbH Germany 2017 Abstract Due to the huge diversity and heterogeneity of data coming from websites and new technologies, data con- tents can be better represented by multiple representations for taking advantage of their complementary characteris- tics efficiently. This paper presents and discusses a new approach for collaborative multi-view clustering based on K-means hypothesis but modified in different ways. Our solution seeks to find a consensus solution from multiple representations by exploiting information from each of them to improve the performance of classical clustering system. To exhibit its effectiveness, the proposed approach is evaluated on two image datasets having different sizes and features. The obtained results reconfirm that multi-view clustering gives performant results and shows that our proposal outperforms mono-view clustering and also several other algorithms in the literature in terms of accuracy, purity and normalized mutual information. Keywords Multi-view clustering · K-means clustering · Collaborative clustering 1 Introduction With the large amount of information, data are more and more disorganized and collected from heterogeneous sources of Communicated by V. Loia. B Safa Bettoumi safa.bettoumi@gmail.com 1 LR-SITI-ENIT (Signal, Images et Technologies de l’information), Ecole Nationale d’Ingnieurs de Tunis, BP-37, Campus Univesitaire, 1002 Tunis, Tunisia information. Thus, new challenges are involved around the clustering problem. It becomes possible and common to have multiple views from the same set of individuals. The possi- bility of having several views naturally offers the possibility of multiple clustering specific to each of them. This leads to several useful combinations of clustering for a wide interpre- tation. Furthermore, the clustering performance can be more accurate by analyzing the affluent information of different views. So, all performances of those clustering results will have to be taken into account to enrich the clustering building process. The multi-view clustering can be found in various disci- plines: economical, social and scientific domains. Recently, results show that multi-view clustering yields more efficient results than mono-view clustering because it represents an additional way to successfully identify good clusters that becomes an asset to have several sources of information. So, a huge amount of results are analyzed to achieve a desired clustering. In scientific communities, multi-view clustering problem is strongly related with the constraint of consensus between views and how to integrate them to conduct the clustering processes to an overall solution. In this context, it is appro- priate to combine clustering results of the same individuals to find a unique clustering by the intermediate of a fusion process to get more confidence in the obtained clusters and reduce the conflict between views. Recently, several fusion approaches of views in the clus- tering process have been proposed. The fusion treatment can be investigated differently relative to the application objec- tive. In general, fusion approaches can be categorized as priori fusion method, posteriori fusion method and a central- ized fusion method, based on their corresponding position from the clustering process. 123