This paper presents partitioning hard kernel clustering algorithms for interval-valued data based on adaptive distances. These adaptive distances are obtained as sums of squared Euclidean distances between interval-valued data computed individually for each interval-valued variable by means of kernel functions. The advantage of the proposed approach over the conventional kernel clustering approaches for interval-valued data is that it allows to learn the relevance weights of the variables during the clustering process, improving the performance of the algorithms. Experiments with real interval-valued data sets show the usefulness of these kernel clustering algorithms.
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