Fuzzy c-means clustering methods for symbolic interval data

This paper presents adaptive and non-adaptive fuzzy c-means clustering methods for partitioning symbolic interval data. The proposed methods furnish a fuzzy partition and prototype for each cluster by optimizing an adequacy criterion based on suitable squared Euclidean distances between vectors of intervals. Moreover, various cluster interpretation tools are introduced. Experiments with real and synthetic data sets show the usefulness of these fuzzy c-means clustering methods and the merit of the cluster interpretation tools.