In this paper we propose a new approach for consensus clustering which is built upon the evidence accumulation framework. Our method takes the co-association matrix as the only input and produces a soft partition of the dataset, where each object is probabilistically assigned to a cluster, as output. Our method reduces the clustering problem to a polynomial optimization in probability domain, which is attacked by means of the Baum-Eagon inequality. Experiments on both synthetic and real benchmarks data, assess the effectiveness of our approach. © 2010 Springer-Verlag Berlin Heidelberg.
CITATION STYLE
Bulò, S. R., Lourenço, A., Fred, A., & Pelillo, M. (2010). Pairwise probabilistic clustering using evidence accumulation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6218 LNCS, pp. 395–404). https://doi.org/10.1007/978-3-642-14980-1_38
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