Abstract
This paper proposes a novel hierarchical clustering algorithm based on high order dissimilarities. These dissimilarity increments are measures computed over triplets of nearest neighbor points. Recently, the distribution of these dissimilarity increments was derived analytically. We propose to incorporate this distribution in a hierarchical clustering algorithm to decide whether two clusters should be merged or not. The proposed algorithm is parameter-free and can identify classes as the union of clusters following the dissimilarity increments distribution. Experimental results show that the proposed algorithm has excellent performance over well separated clusters, also providing a good hierarchical structure insight into touching clusters.
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Aidos, H., Fred, A. (2011). Hierarchical Clustering with High Order Dissimilarities. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2011. Lecture Notes in Computer Science(), vol 6871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23199-5_21
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DOI: https://doi.org/10.1007/978-3-642-23199-5_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-23198-8
Online ISBN: 978-3-642-23199-5
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