A fuzzy clustering algorithm for symbolic interval data based on a single adaptive euclidean distance

Abstract

The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents a fuzzy c-means clustering algorithm for symbolic interval data. This method furnishes 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 a suitable single adaptive Euclidean distance between vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method. © Springer-Verlag Berlin Heidelberg 2006.