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A multi-view relational fuzzy c-medoid vectors clustering algorithm.pdf

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A multi-view relational fuzzy c-medoid vectors clustering algorithm.pdf

Neurocomputing 163 (2015) 115–123 Contents lists available at ScienceDirect Neurocomputing journal homepage: /locate/neucom A multi-view relational fuzzy c-medoid vectors clustering algorithm Francisco de A.T. de Carvalho a,n, Filipe M. de Melo a, Yves Lechevallier b a Centro de Informática, Universidade Federal de Pernambuco, Av. Jornalista Anibal Fernandes, s/n - Cidade Universitária, CEP 50740-560, Recife (PE), Brazil b INRIA Paris-Rocquencourt, Domaine de Voluceau, BP 105, 78153 Le Chesnay, France article info Article history: Received 15 January 2014 Received in revised form 7 August 2014 Accepted 1 November 2014 Available online 14 April 2015 Keywords: Fuzzy c-medoid vectors Multi-view clustering Collaborative clustering Relational clustering Relevance weights abstract This paper gives a multi-view relational fuzzy c-medoid vectors clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a ?nal consensus fuzzy partition. These matrices could have been obtained using different sets of variables and dissimilarity functions. This algorithm is designed to give a fuzzy partition and a vector of medoids for each fuzzy cluster as well as to learn a relevance weight for each dissimilarity matrix by optimizing an objective function. These relevance weights change at each iteration of the algorithm and are different from one cluster to another. Moreover, various tools for interpreting the fuzzy partition and fuzzy clusters provided by this algorithm are also presented. Several examples illustrate the performance and usefulness of the proposed algorithm. 2015 Elsevier B.V. All rights reserved. 1. Introduction Clustering methods seek to organize a set of items into clusters such that items within a given cluster have a high degree of similarity, whereas items belonging to different clusters have a high degre