Self-organizing map based on L2 distance for interval-valued data

The Self-Organizing Maps have been widely used as multidimensional unsupervised classifiers. The aim of this paper is to develop a self-organizing map for interval data. Due to the increasing use of such data in Data Mining, many clustering methods for interval data have been proposed this last decade. In this paper, we propose an algorithm to train the self-organizing map for interval data. We use an extension of the Euclidian distance to compare two vectors of intervals. In order to show the usefulness of our approach, we apply the proposed algorithm on real interval data issued from meteorological stations in China.