Midpoint radius self-organizing maps for interval-valued data with telecommunications application

The aim of this paper is to cluster units (objects) described by interval-valued information by adopting an unsupervised neural network approach. By considering a suitable distance measure for interval data, self-organizing maps to deal with interval-valued data are suggested. The technique, called midpoint radius self-organizing maps (MR-SOMs), recovers the underlying structure of interval-valued data by using both the midpoints (or centers) and the radii (a measure of the interval width) information. In order to show how the method MR-SOMs works a suggestive application on telecommunication market segmentation is described.