Performance Assessment of Kernel-Based Clustering

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 246)

Abstract

Kernel methods are ones that, by replacing the inner product with positive definite function, implicitly perform a nonlinear mapping of input data into a high-dimensional feature space. Various types of kernel-based clustering methods have been studied so far by many researchers, where Gaussian kernel, in particular, has been found to be useful. In this paper, we have investigated the role of kernel function in clustering and incorporated different kernel functions. We discussed numerical results in which different kernel functions are applied to kernel-based hybrid c-means clustering. Various synthetic data sets and real-life data set are used for analysis. Experiments results show that there exist other robust kernel functions which hold like Gaussian kernel.

Keywords

Clustering Kernel function Gaussian kernel Hyper-tangent kernel Log kernel 

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Copyright information

© Springer India 2014

Authors and Affiliations

  1. 1.Maharaja Surajmal Institute of TechnologyNew DelhiIndia
  2. 2.Netaji Subash Institute of TechnologyNew DelhiIndia

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