Abstract: A new kernel based fuzzy clustering algorithm was proposed on the basis of combining the kernel technique with the rules of the im proved fuzzy c-means clustering algorithm. In the proposed algorithm, the sample points are mapped into the feature space via introducing the kernel function into the clustering model to improve the performance. The new algorithm is robust to the noises because it relaxes the constraint conditions used in the fuzzy c-means clustering model. We compared the results with those of some most frequently used clustering algorithms to show the effectiveness of the proposed algorithm.

Key words: cluster analysis, kernel function, fuzzy c-means, feature space