A Novel Kernelized Fuzzy Clustering Algorithm for Data Classification

Data are expanding day by day, clustering plays a main role in handling the data and to discover knowledge from it. Most of the clustering approaches deal with the linear separable problems. To deal with the nonlinear separable problems, we introduce the concept of kernel function in fuzzy clustering. In Kernelized fuzzy clustering approach the kernel function defines the non- linear transformation that projects the data from the original space where the data are can be more separable. The proposed approach uses kernel methods to project data from the original space to a high dimensional feature space where data can be separable linearly. We performed the test on the real world datasets which shows that our proposed kernel based clustering method gives better accuracy as compared to the fuzzy clustering method.