Kernel based K-means clustering using rough set

From the beginning of the data analysis system cluster computing plays an important role on it. The very early developed clustering algorithms which can handle only numerical data and K-means clustering is one of them and was proposed by Macqueen [1] in 1967. This algorithm helps us to find the homogeneity of the data set. This K-means algorithm has been modified in many ways to get the modified K-means and kernel based K-means is one of them. It is a nonlinear transformation which transforms the sample data into high dimensional feature space. Though this kernel based K-means performs good almost on every data set but it is unable to handle uncertainty. After rough set theory has been proposed by Pawlak [2], we have many clustering algorithms based on it which can handle uncertainty and heterogeneous data and Rough based K-means is one of them. So in this paper we are proposing the combination of these two methods and known as kernel based K-Means using rough set.