Abstract
Recently, clustering algorithms based on rough set theory have gained increasing attention. For example, Lingras et al. introduced a rough k-means that assigns objects to lower and upper approximations of clusters. The objects in the lower approximation surely belong to a cluster while the membership of the objects in an upper approximation is uncertain. Therefore, the core cluster, defined by the objects in the lower approximation is surrounded by a buffer or boundary set with objects with unclear membership status. In this paper, we introduce an evolutionary rough k-medoid clustering algorithm. Evolutionary rough k-medoid clustering belongs to the families of Lingras’ rough k-means and classic k-medoids algorithms. We apply the evolutionary rough k-medoids to synthetic as well as to real data sets and compare the results to Lingras’ rough k-means. We also introduce a rough version of the Davies-Bouldin-Index as a cluster validity index for the family of rough clustering algorithms.
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Peters, G., Lampart, M., Weber, R. (2008). Evolutionary Rough k-Medoid Clustering. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets VIII. Lecture Notes in Computer Science, vol 5084. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85064-9_13
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DOI: https://doi.org/10.1007/978-3-540-85064-9_13
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