Self-Organizing Map Based on City-Block Distance for Interval-Valued Data

Conference paper

Abstract

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 the city-block distance to compare two vectors of intervals. In order to show the usefulness of our approach, we apply the self-organizing map on real interval data issued from meteorological stations in France.

Keywords

Input Vector Interval Data Symbolic Data Neighborhood Function Pattern Recognition Letter 
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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Copyright information

© Springer Berlin Heidelberg 2012

Authors and Affiliations

  1. 1.Signal Processing and Electronic Systems DepartmentSUPELEC Systems Sciences (E3S)Gif-sur-Yvette cedexFrance
  2. 2.Université LibanaiseBeirutLebanon

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