Dynamic clustering of interval data based on adaptive Chebyshev distances

A dynamic clustering method for interval data is presented. This method furnishes a partition and a prototype for each cluster by optimising a criterion based on adaptive Chebyshev distances. Experiments with real and artificial interval data sets have demonstrated the usefulness of this method.