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.