Abstract
It has been observed that, in general, the performance of clustering algorithms degrades if the clusters in a dataset overlap, i.e., there are many points in the dataset that have significant membership in multiple classes (SiMM). It leads to a lot of confusion regarding their cluster assignments. Hence, it may be beneficial if these points are first identified and excluded from consideration while clustering the dataset. In the next stage, they can be assigned to a cluster using some classifier trained by the remaining points. In this work, a Support Vector Machine- (SVM-)based classifier [417] has been utilized for this purpose.
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© 2011 Springer-Verlag Berlin Heidelberg
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Maulik, U., Bandyopadhyay, S., Mukhopadhyay, A. (2011). Two-Stage Fuzzy Clustering. In: Multiobjective Genetic Algorithms for Clustering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16615-0_7
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DOI: https://doi.org/10.1007/978-3-642-16615-0_7
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Publisher Name: Springer, Berlin, Heidelberg
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Online ISBN: 978-3-642-16615-0
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