Probabilistic Evidence Accumulation for Clustering Ensembles

André Lourenço, Samuel Rota Bulò, Nicola Rebagliati, Ana Fred, Mário Figueiredo, Marcello Pelillo


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 maximum 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.


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Paper Citation

in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 58-67. DOI: 10.5220/0004267900580067

in Bibtex Style

author={André Lourenço 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 - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Probabilistic Evidence Accumulation for Clustering Ensembles
SN - 978-989-8565-41-9
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