Abstract
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.
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Aidos, H., Fred, A. (2013). Dissimilarity Increments Distribution in the Evidence Accumulation Clustering Framework. In: Sanches, J.M., Micó, L., Cardoso, J.S. (eds) Pattern Recognition and Image Analysis. IbPRIA 2013. Lecture Notes in Computer Science, vol 7887. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38628-2_63
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DOI: https://doi.org/10.1007/978-3-642-38628-2_63
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-38627-5
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