Abstract
This paper proposes a statistical model for the dissimilarity changes (increments) between neighboring patterns which follow a 2-dimensional Gaussian distribution. We propose a novel clustering algorithm, using that statistical model, which automatically determines the appropriate number of clusters. We apply the algorithm to both synthetic and real data sets and compare it to a Gaussian mixture and to a previous algorithm which also used dissimilarity increments. Experimental results show that this new approach yields better results than the other two algorithms in most datasets.
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Aidos, H., Fred, A. (2011). On the Distribution of Dissimilarity Increments. In: Vitrià, J., Sanches, J.M., Hernández, M. (eds) Pattern Recognition and Image Analysis. IbPRIA 2011. Lecture Notes in Computer Science, vol 6669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21257-4_24
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DOI: https://doi.org/10.1007/978-3-642-21257-4_24
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-21256-7
Online ISBN: 978-3-642-21257-4
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