Abstract
In this paper we propose a way of handling fuzziness while mining large data. Clustering Large Applications based on RANdomized Search (CLARANS) is enhanced to incorporate the fuzzy component. A new scalable approximation to the maximum number of neighbours, explored at a node, is developed. The goodness of the generated clusters is evaluated in terms of validity indices. Experimental results on various data sets is run to converge to the optimal number of partitions.
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Ghosh, S., Mitra, S. (2009). Incorporating Fuzziness to CLARANS. In: Chaudhury, S., Mitra, S., Murthy, C.A., Sastry, P.S., Pal, S.K. (eds) Pattern Recognition and Machine Intelligence. PReMI 2009. Lecture Notes in Computer Science, vol 5909. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11164-8_20
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DOI: https://doi.org/10.1007/978-3-642-11164-8_20
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11163-1
Online ISBN: 978-3-642-11164-8
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