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Kohonen neural networks for interval-valued data clustering

Abstract : Kohonen neural Networks have been widely used as multidimensional unsupervised classifiers. The aim of this paper is to develop a kohonen network 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 Kohonen Network in order to cluster interval data while preserving the topology of the data. Similar data vectors will be allocated to same neuron or to a neighbor neuron on the Kohonen network. 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 Chinese and Lebanese meteorological stations.
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Submitted on : Tuesday, September 17, 2013 - 9:48:03 AM
Last modification on : Monday, December 14, 2020 - 12:38:06 PM


  • HAL Id : hal-00862567, version 1



Hani Hamdan, Chantal Hajjar. Kohonen neural networks for interval-valued data clustering. International Journal of Advanced Computer Science, 2012, 2 (11), pp.412-419. ⟨hal-00862567⟩



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