Abstract
This paper presents a clustering method for interval-valued data using a dynamic cluster algorithm with adaptive squared Euclidean distances. This method furnishes a partition and a prototype to each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare a class with its representative, the method uses an adaptive version of a squared Euclidean distance to interval-valued data. Experiments with real and artificial interval-valued data sets shows the usefulness of the this method.
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