Exploring different kernel functions for kernel-based clustering
Meena Tushir, Smriti Srivastava · International Journal of Artificial Intelligence and Soft Computing · 2016
Kernel methods are ones that, by replacing the inner product with positive definite function, implicitly perform a non-linear mapping of input data into a high dimensional feature space. Kernel functions are used to make this mapping in higher dimension redundant. These kernel functions play an important role in classification. The kernel-based clustering methods are found to be superior in accuracy to the conventional ones. The choice of kernel function is neither easy nor trivial. 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 study, we present a comprehensive comparative analysis of kernel based hybrid c-means clustering using different kernel functions. We have incorporated Mercer kernel functions (positive definite kernels) as well as conditionally positive definite kernel functions. Various synthetic datasets and real-life datasets are used for analysis. Experiments results show that there exist other robust kernel functions which hold like Gaussian kernel.