Clustering of Interval Data Using Self-Organizing Maps – Application to Meteorological Data

Part of the Topics in Intelligent Engineering and Informatics book series (TIEI, volume 1)

Abstract

The self-organizing map is a kind of artificial neural network used to map high dimensional data into a low dimensional space. This chapter presents a self-organizing map to do unsupervised clustering for interval data. This map uses an extension of the Euclidian distance to compute the proximity between two vectors of intervals where each neuron represents a cluster. The performance of this approach is then illustrated and discussed while applied to temperature interval data coming from Chinese meteorological stations. The bounds of each interval are the measured minimal and maximal values of the temperature. In the presented experiments, stations of similar climate regions are assigned to the same neuron or to a neighbor neuron on the map.

Keywords

Input Vector Hausdorff Distance Interval Data Neighborhood Function Neighborhood Radius 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© Springer-Verlag GmbH Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Department of Signal Processing and Electronic SystemsSUPELECGif-sur-Yvette cedexFrance
  2. 2.Université LibanaiseBeirutLebanon

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