Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Neural Networks
On the ordering conditions for self-organizing maps
Neural Computation
Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
Ordering of self-organizing maps in multidimensional cases
Neural Computation
Neural maps and topographic vector quantization
Neural Networks
On Interval Weighted Three-Layer Neural Networks
SS '98 Proceedings of the The 31st Annual Simulation Symposium
Magnification Control in Self-Organizing Maps and Neural Gas
Neural Computation
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A robust fuzzy k-means clustering model for interval valued data
Computational Statistics
An extended self-organizing map network for market segmentation: a telecommunication example
Decision Support Systems
Self-Organizing Maps for imprecise data
Fuzzy Sets and Systems
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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.