Fuzzy clustering algorithms for symbolic interval data based on adaptive and non-adaptive Euclidean distances

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents fuzzy c-means clustering algorithms for symbolic interval data. The proposed methods furnish a partition of the input data and a corresponding prototype (a vector of intervals) for each class by optimizing an adequacy criterion which is based on adaptive and non-adaptive Euclidean distance between vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.