Publication
Probabilistic consensus clustering using evidence accumulation
| dc.contributor.author | Lourenço, André Ribeiro | |
| dc.contributor.author | Bulo, Samuel Rota | |
| dc.contributor.author | Rebagliati, Nicola | |
| dc.contributor.author | Fred, Ana | |
| dc.contributor.author | Figueiredo, Mário | |
| dc.contributor.author | Pelillo, Marcello | |
| dc.date.accessioned | 2016-02-25T17:11:54Z | |
| dc.date.available | 2016-02-25T17:11:54Z | |
| dc.date.issued | 2015-01 | |
| dc.description.abstract | 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. | pt_PT |
| dc.identifier.citation | LOURENÇO, André; [et al.] - Probabilistic consensus clustering using evidence accumulation. Machine Learning. ISSN. 0885-6125. Vol. 98, N.º 1-2, SI (2015), pp. 331-357 | pt_PT |
| dc.identifier.doi | 10.1007/s10994-013-5339-6 | pt_PT |
| dc.identifier.issn | 0885-6125 | |
| dc.identifier.issn | 1573-0565 | |
| dc.identifier.uri | http://hdl.handle.net/10400.21/5750 | |
| dc.language.iso | eng | pt_PT |
| dc.peerreviewed | yes | pt_PT |
| dc.publisher | Springer | pt_PT |
| dc.relation.ispartofseries | SI; | |
| dc.relation.publisherversion | http://link.springer.com/article/10.1007%2Fs10994-013-5339-6 | pt_PT |
| dc.subject | Consensus clustering | pt_PT |
| dc.subject | Evidence Accumulation | pt_PT |
| dc.subject | Ensemble clustering | pt_PT |
| dc.subject | Bregman divergence | pt_PT |
| dc.title | Probabilistic consensus clustering using evidence accumulation | pt_PT |
| dc.type | journal article | |
| dspace.entity.type | Publication | |
| oaire.citation.endPage | 357 | pt_PT |
| oaire.citation.issue | 1-2 | pt_PT |
| oaire.citation.startPage | 331 | pt_PT |
| oaire.citation.volume | 98 | pt_PT |
| rcaap.rights | closedAccess | pt_PT |
| rcaap.type | article | pt_PT |
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