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Dynamic clustering for interval data based on L 2 distance

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Summary

This paper introduces a partitioning clustering method for objects described by interval data. It follows the dynamic clustering approach and uses and L 2 distance. Particular emphasis is put on the standardization problem where we propose and investigate three standardization techniques for interval-type variables. Moreover, various tools for cluster interpretation are presented and illustrated by simulated and real-case data.

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Acknowledgements

The first author would like to thank CNPq (Brazilian Agency) for its financial support. The second author would like to thank Calouste Gulbenkian Foundation and FCT/MCTES (Portuguese Agency).

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Authors and Affiliations

  1. Centro de Informática-CIn/UFPE, Av. Prof. Luiz Freire, s/n, Cidade Universitária, CEP: 50740-540, Recife-PE, Brasil

    Francisco de A. T. de Carvalho

  2. Faculdade de Economia/NIAAD-LIACC, Universidade do Porto, Rua Dr. Roberto Frias, 4200-464, Porto, Portugal

    Paula Brito

  3. Institute of Statistics, RWTH Aachen University, 52056, Aachen, Germany

    Hans-Hermann Bock

Authors
  1. Francisco de A. T. de Carvalho
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  2. Paula Brito
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  3. Hans-Hermann Bock
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de Carvalho, F.d.A.T., Brito, P. & Bock, HH. Dynamic clustering for interval data based on L 2 distance. Computational Statistics 21, 231–250 (2006). https://doi.org/10.1007/s00180-006-0261-z

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  • DOI: https://doi.org/10.1007/s00180-006-0261-z

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