caa a22 4500 605478112 CHVBK 20210128100404.0 cr unu---uuuuu 210128e20150101xx s 000 0 eng 10.1007/s10994-013-5339-6 doi (NATIONALLICENCE)springer-10.1007/s10994-013-5339-6 Probabilistic consensus clustering using evidence accumulation [Elektronische Daten] [André Lourenço, Samuel Rota Bulò, Nicola Rebagliati, Ana Fred, Mário Figueiredo, Marcello Pelillo] Clustering ensemble methods produce a consensus partition of a set of data points by combining the results of a collection of base clustering algorithms. In the evidence accumulation clustering (EAC) paradigm, the clustering ensemble is transformed into a pairwise co-association matrix, thus avoiding the label correspondence problem, which is intrinsic to other clustering ensemble schemes. In this paper, we propose a consensus clustering approach based on the EAC paradigm, which is not limited to crisp partitions and fully exploits the nature of the co-association matrix. Our solution determines probabilistic assignments of data points to clusters by minimizing a Bregman divergence between the observed co-association frequencies and the corresponding co-occurrence probabilities expressed as functions of the unknown assignments. We additionally propose an optimization algorithm to find a solution under any double-convex Bregman divergence. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach. The Author(s), 2013 Consensus clustering nationallicence Evidence Accumulation nationallicence Ensemble clustering nationallicence Bregman divergence nationallicence Lourenço André Instituto Superior de Engenharia de Lisboa, Lisboa, Portugal aut Rota Bulò Samuel DAIS, via Torino, 155, Mestre, Venezia, Italy aut Rebagliati Nicola VTT Technical Research Center of Finland, P.O. Box 1000, 02044, VTT, Finland aut Fred Ana Instituto de Telecomunicações, Lisboa, Portugal aut Figueiredo Mário Instituto de Telecomunicações, Lisboa, Portugal aut Pelillo Marcello DAIS, via Torino, 155, Mestre, Venezia, Italy aut Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 331-357 0885-6125 98:1-2<331 2015 98 10994 https://doi.org/10.1007/s10994-013-5339-6 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s10994-013-5339-6 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Lourenço André Instituto Superior de Engenharia de Lisboa, Lisboa, Portugal aut NATIONALLICENCE 700 1- Rota Bulò Samuel DAIS, via Torino, 155, Mestre, Venezia, Italy aut NATIONALLICENCE 700 1- Rebagliati Nicola VTT Technical Research Center of Finland, P.O. Box 1000, 02044, VTT, Finland aut NATIONALLICENCE 700 1- Fred Ana Instituto de Telecomunicações, Lisboa, Portugal aut NATIONALLICENCE 700 1- Figueiredo Mário Instituto de Telecomunicações, Lisboa, Portugal aut NATIONALLICENCE 700 1- Pelillo Marcello DAIS, via Torino, 155, Mestre, Venezia, Italy aut NATIONALLICENCE 773 0- Machine Learning Springer US; http://www.springer-ny.com 98/1-2(2015-01-01), 331-357 0885-6125 98:1-2<331 2015 98 10994