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Authors: André Lourenço 1 ; Samuel Rota Bulò 2 ; Nicola Rebagliati 2 ; Ana Fred 3 ; Mário Figueiredo 3 and Marcello Pelillo 2

Affiliations: 1 Instituto Superior de Engenharia de Lisboa and Instituto Superior Técnico, Portugal ; 2 Università Ca’ Foscari Venezia, Italy ; 3 Instituto Superior Técnico, Portugal

Keyword(s): Clustering Algorithm, Clustering Ensembles, Probabilistic Modeling, Evidence Accumulation Clustering.

Related Ontology Subjects/Areas/Topics: Clustering ; Ensemble Methods ; Pattern Recognition ; Theory and Methods

Abstract: Ensemble clustering methods derive a consensus partition of a set of objects starting from the results of a collection of base clustering algorithms forming the ensemble. Each partition in the ensemble provides a set of pairwise observations of the co-occurrence of objects in a same cluster. The evidence accumulation clustering paradigm uses these co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix, which is fed to a pairwise similarity clustering algorithm to obtain a final consensus clustering. The advantage of this solution is the avoidance of the label correspondence problem, which affects other ensemble clustering schemes. In this paper we derive a principled approach for the extraction of a consensus clustering from the observations encoded in the co-association matrix. We introduce a probabilistic model for the co-association matrix parameterized by the unknown assignments of objects to clusters, which are in turn estimated using a maxi mum likelihood approach. Additionally, we propose a novel algorithm to carry out the parameter estimation with convergence guarantees towards a local solution. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach. (More)


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Paper citation in several formats:
Lourenço, A.; Rota Bulò, S.; Rebagliati, N.; Fred, A.; Figueiredo, M. and Pelillo, M. (2013). Probabilistic Evidence Accumulation for Clustering Ensembles. In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM, ISBN 978-989-8565-41-9; ISSN 2184-4313, pages 58-67. DOI: 10.5220/0004267900580067

author={André Louren\c{C}o. and Samuel {Rota Bulò}. and Nicola Rebagliati. and Ana Fred. and Mário Figueiredo. and Marcello Pelillo.},
title={Probabilistic Evidence Accumulation for Clustering Ensembles},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM,},


JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - ICPRAM,
TI - Probabilistic Evidence Accumulation for Clustering Ensembles
SN - 978-989-8565-41-9
IS - 2184-4313
AU - Lourenço, A.
AU - Rota Bulò, S.
AU - Rebagliati, N.
AU - Fred, A.
AU - Figueiredo, M.
AU - Pelillo, M.
PY - 2013
SP - 58
EP - 67
DO - 10.5220/0004267900580067