Dissimilarity Increments Distribution in the Evidence Accumulation Clustering Framework
Fred, A. L. N.
Dissimilarity Increments Distribution in the Evidence Accumulation Clustering Framework, Proc Iberian Conf. on Pattern Recognition and Image Analysis, Funchal, Madeira, Portugal, Vol. 7887, pp. 535 - 542, June, 2013.
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In this paper, we combine two concepts. The first is the Evidence Accumulation Clustering framework, which uses a voting scheme to combine clustering ensembles and produce a co-association matrix. The second concept are Dissimilarity Increments, which are a high order dissimilarity measure which can identify sparse clusters, since it uses three data points at a time instead of two points, as in Euclidean distance. These two concepts are combined to form a new family of clustering algorithms, where the co-association matrix is used to form a distance which is then used to compute dissimilarity increments. These clustering algorithms are shown to improve the clustering results when compared to the usual Evidence Accumulation Clustering framework.