An intelligent Weighted Kernel K-Means algorithm for high dimension data

Clustering is a kind of unsupervised classification of objects into groups so that objects from the same cluster are more similar to each other than objects from different clusters. In this paper, we focus on Weighted Kernel K-Means method for its capability to handle nonlinear separability, noise, outliers and high dimensionality in the data. A new WKM algorithm has been proposed and tested on real Rice data. the results exposed by algorithm encourage the use of WKM for the solution of real world problems.