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Consensus Clustering using Partial Evidence Accumulation

Lourenço, A. ; Bulo, S. Bulo ; Rebagliati, N. ; Fred, A. L. N. ; Figueiredo, M. A. T. ; Pelillo, M.

Consensus Clustering using Partial Evidence Accumulation, Proc Iberian Conf. on Pattern Recognition and Image Analysis, Funchal, Portugal, Vol. ?, pp. ? - ?, June, 2013.

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The Evidence Accumulation Clustering, EAC, algorithm is a clustering ensemble method which uses co-occurrence statistics to derive a similarity matrix, referred to as co-association matrix. In order to obtain a final consensus clustering the co-association matrix is fed to a pairwise similarity clustering algorithm. The method has thus O(n^2) space complexity, which can constitute a relevant bottleneck to its scalability. In this paper we propose a new formulation which works using a partial set of the co-occurrences, greatly reducing the computational time and space, leading to a scalable algorithm. Experiments on both synthetic and real benchmark data show the effectiveness of the proposed approach.