A Novel Kernel Clustering Algorithm

Abstract

K-means algorithm is one of the most famous clustering algorithms in data mining due to its simplicity.Kernel K-means is an extension of K-means to cluster nonlinear separable data.However, it still has some limitations like sensitivity and convergence to the local optima.In this paper, we show how to implement a new novel kernel-clustering algorithm that is robust and converges to the global solution.We show using artificial and real data sets that the proposed kernel algorithm performs better than the standard kernel K-means algorithm.