A Fuzzy Clustering Algorithm for Symbolic Interval Data Based on a Single Adaptive Euclidean Distance

  • Francisco de A.T. de Carvalho
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)


The recording of symbolic interval data has become a common practice with the recent advances in database technologies. This paper presents a fuzzy c-means clustering algorithm for symbolic interval data. This method furnishes a partition of the input data and a corresponding prototype (a vector of intervals) for each class by optimizing an adequacy criterion which is based on a suitable single adaptive Euclidean distance between vectors of intervals. Experiments with real and synthetic symbolic interval data sets showed the usefulness of the proposed method.


Fuzzy Cluster Interval Data Fuzzy Partition Fuzzy Cluster Algorithm Adequacy Criterion 
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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Francisco de A.T. de Carvalho
    • 1
  1. 1.Cidade Universitaria, CEPCentro de Informatica – CIn/UFPERecife-PEBrazil

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