Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set

Áurea Sousa; Helena Bacelar-Nicolau; Fernando C. Nicolau; Osvaldo Silva

Biometrical Letters (2013)

  • Volume: 50, Issue: 1, page 27-38
  • ISSN: 1896-3811

Abstract

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In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors.

How to cite

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Áurea Sousa, et al. "Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set." Biometrical Letters 50.1 (2013): 27-38. <http://eudml.org/doc/268875>.

@article{ÁureaSousa2013,
abstract = {In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors.},
author = {Áurea Sousa, Helena Bacelar-Nicolau, Fernando C. Nicolau, Osvaldo Silva},
journal = {Biometrical Letters},
keywords = {Ascendant Hierarchical Cluster Analysis; Symbolic Data; Interval Data; Affinity Coefficient; VL Methodology},
language = {eng},
number = {1},
pages = {27-38},
title = {Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set},
url = {http://eudml.org/doc/268875},
volume = {50},
year = {2013},
}

TY - JOUR
AU - Áurea Sousa
AU - Helena Bacelar-Nicolau
AU - Fernando C. Nicolau
AU - Osvaldo Silva
TI - Clustering of Symbolic Data based on Affinity Coefficient: Application to a Real Data Set
JO - Biometrical Letters
PY - 2013
VL - 50
IS - 1
SP - 27
EP - 38
AB - In this paper, we illustrate an application of Ascendant Hierarchical Cluster Analysis (AHCA) to complex data taken from the literature (interval data), based on the standardized weighted generalized affinity coefficient, by the method of Wald and Wolfowitz. The probabilistic aggregation criteria used belong to a parametric family of methods under the probabilistic approach of AHCA, named VL methodology. Finally, we compare the results achieved using our approach with those obtained by other authors.
LA - eng
KW - Ascendant Hierarchical Cluster Analysis; Symbolic Data; Interval Data; Affinity Coefficient; VL Methodology
UR - http://eudml.org/doc/268875
ER -

References

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  2. Bacelar-Nicolau H. (1987): On the Distribution Equivalence in Cluster Analysis, Proc. of the NATO ASI on Pattern Recognition Theory and Applications, Springer- Verlag, New York, 1987: 73-79. 
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  4. Bacelar-Nicolau H. (2000): The Affinity Coefficient. In: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, H.-H. Bock and E. Diday (Eds.), Berlin: Springer-Verlag: 160-165. Zbl0977.62066
  5. Bacelar-Nicolau H. (2002): On the Generalised Affinity Coefficient for Complex Data. Biocybernetics and Biomedical Engineering 22(1): 31-42. 
  6. Bacelar-Nicolau H., Nicolau F.C., Sousa A., Bacelar-Nicolau L. (2009): Measuring Similarity of Complex and Heterogeneous Data in Clustering of Large Data Sets, Biocybernetics and Biomedical Engineering 29(2): 9-18. Zbl1286.62060
  7. Bacelar-Nicolau H., Nicolau F.C., Sousa A., Bacelar-Nicolau L. (2010): Clustering Complex Heterogeneous Data Using a Probabilistic Approach. Proceedings of Stochastic Modeling Techniques and Data Analysis International Conference (SMTDA2010), Chania Crete Greece, 8-11 June 2010 - published on the CD Proceedings of SMTDA2010 (electronic publication). Zbl1286.62060
  8. Bock H.-H., Diday E. (2000): Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data. Series: Studies in Classification, Data Analysis, and Knowledge Organization, Berlin: Springer- Verlag. Zbl1039.62501
  9. Chavent M., Lechevallier Y. (2002): Dynamical Clustering Algorithm of Interval Data: Optimization of an Adequacy Criterion Based on Hausdorff Distance. In: Classification, Clustering, and Data Analysis, K. Jajuga, A. Sokolowski, H.-H. Bock (Eds.), Berlin: Springer-Verlag: 53-60. Zbl1032.62058
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  14. Fraser D.A.S. (1975): Non Parametric Methods in Statistics. Chapman and Hall. 
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  19. Nicolau F.C.m, Bacelar-Nicolau H. (1998): Some Trends in the Classification of Variables. In: Data Science, Classification, and Related Methods, C. Hayashi, N. Ohsumi, K. Yajima, Y. Tanaka, H.-H. Bock, Y. Baba (Eds.), Springer-Verlag: 89-98. Zbl0894.62075
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