A Kernel-Based Fuzzy Clustering Algorithm

Abstract

We propose a kernel-based fuzzy clustering algorithm to cluster data in the feature space. Our method uses kernel functions to project data from the original space into a high dimensional feature space, and then divides them into groups through their similarities in the feature space with an incremental clustering approach. After clustering, data patterns of the same cluster in the feature space are then grouped with an arbitrarily shaped boundary in the original space. The effectiveness of our method is demonstrated in the experiments.