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.
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Peters, G. (2005). Outliers in Rough k-Means Clustering. In: Pal, S.K., Bandyopadhyay, S., Biswas, S. (eds) Pattern Recognition and Machine Intelligence. PReMI 2005. Lecture Notes in Computer Science, vol 3776. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11590316_113
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DOI: https://doi.org/10.1007/11590316_113
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-30506-4
Online ISBN: 978-3-540-32420-1
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