Abstract
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.
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Bulò, S.R., Lourenço, A., Fred, A., Pelillo, M. (2010). Pairwise Probabilistic Clustering Using Evidence Accumulation. In: Hancock, E.R., Wilson, R.C., Windeatt, T., Ulusoy, I., Escolano, F. (eds) Structural, Syntactic, and Statistical Pattern Recognition. SSPR /SPR 2010. Lecture Notes in Computer Science, vol 6218. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-14980-1_38
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DOI: https://doi.org/10.1007/978-3-642-14980-1_38
Publisher Name: Springer, Berlin, Heidelberg
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