A novel adaptive fuzzy c-means algorithm for interval data type

A novel extension of the fuzzy c-means clustering algorithm for interval data type based on an adaptive Euclidean distance is presented. The proposed method furnishes a fuzzy partition and a prototype for each cluster by optimizing a criterion based on an adaptive Euclidean distance that changes at each algorithm iteration. Experiments with real and synthetic data sets show the usefulness of this method.

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