Kernel fuzzy clustering methods based on local adaptive distances

This paper presents kernel fuzzy clustering methods in which dissimilarity measures are obtained as sums of squared Euclidean distances between patterns and centroids computed individually for each variable by means of kernel functions. The advantage of the proposed approach over the conventional kernel clustering methods is that it allows us to use adaptive distances which changes at each algorithm iteration and can be different from one cluster to another. This kind of dissimilarity measure is suitable to learn the weights of the variables during the clustering process, improving the performance of the algorithms. Another advantage of this approach is that it allows the introduction of various fuzzy partition and cluster interpretations tools. Experiments with benchmark data sets illustrate the usefulness of our algorithms and the merit of the fuzzy partition and cluster interpretation tools.