Clustering of Interval-Valued Data Using Adaptive Squared Euclidean Distances

  • Renata M. C. R. de Souza
  • Francisco de A.T. de Carvalho
  • Fabio C. D. Silva
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3316)

Abstract

This paper presents a clustering method for interval-valued data using a dynamic cluster algorithm with adaptive squared Euclidean distances. This method furnishes a partition and a prototype to each cluster by optimizing an adequacy criterion that measures the fitting between the clusters and their representatives. To compare a class with its representative, the method uses an adaptive version of a squared Euclidean distance to interval-valued data. Experiments with real and artificial interval-valued data sets shows the usefulness of the this method.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Renata M. C. R. de Souza
    • 1
  • Francisco de A.T. de Carvalho
    • 1
  • Fabio C. D. Silva
    • 1
  1. 1.Centro de Informatica – CIn / UFPERecifeBrasil

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