Abstract
Recently rough cluster algorithm were introduced and successfully applied to real life data. In this paper we analyze the rough k-means introduced by Lingras' et al. with respect to its compliance to the classical k-means, the numerical stability and its performance in the presence of outliers. We suggest a variation of the algorithm that shows improved results in these circumstances. © Springer-Verlag Berlin Heidelberg 2005.
Cite
CITATION STYLE
Peters, G. (2005). Outliers in rough k-means clustering. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 3776 LNCS, pp. 702–707). https://doi.org/10.1007/11590316_113
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