Partitioning hard kernel clustering methods based on local adaptive distances

This paper presents partitioning hard kernel 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 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 partition and cluster interpretations tools. Experiments with benchmark data sets illustrate the usefulness of our algorithms and the merit of the partition and cluster interpretation tools.