A Novel Kernel Clustering Algorithm

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.