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

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

Biometrical Letters
Volume 50 (2013): Issue 1 (June 2013)
By: Áurea Sousa,  Helena Bacelar-Nicolau,  Fernando C. Nicolau and  Osvaldo Silva  
Open Access
|Jun 2013

References

  1. Bacelar-Nicolau H. (1980): Contributions to the Study of Comparison Coefficients in Cluster Analysis, PhD Th. (in Portuguese), Univ. Lisbon.
    Search in Google Scholar
  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: 73-79.10.1007/978-3-642-83069-3_7
    Search in Google Scholar
  3. Bacelar-Nicolau H. (1988): Two Probabilistic Models for Classification of Variables in Frequency Tables. In: Classification and Related Methods of Data Analysis, H.-H. Bock (ed.), North Holland: Elsevier Sciences Publishers B.V.: 181-186.
    Search in Google Scholar
  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.
    Search in Google Scholar
  5. Bacelar-Nicolau H. (2002): On the Generalised Affinity Coefficient for Complex Data. Biocybernetics and Biomedical Engineering 22(1): 31-42.
    Search in Google Scholar
  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.
    Search in Google Scholar
  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).
    Search in Google Scholar
  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.10.1007/978-3-642-57155-8
    Search in Google Scholar
  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: 53-60.10.1007/978-3-642-56181-8_5
    Search in Google Scholar
  10. Chavent M., De Carvalho F.A.T., Lechevallier Y., Verde R. (2003): Trois Nouvelles Méthodes de Classification Automatique de Données Symboliques de type intervalle, Revue de Statistique Appliquée, tome 51(4): 5-29.
    Search in Google Scholar
  11. De Carvalho F.A.T., Brito P., Bock H-H. (2006a): Dynamic Clustering for Interval Data Based on L2 Distance. Computational Statistics 21(2).10.1007/s00180-006-0261-z
    Search in Google Scholar
  12. De Carvalho F.A.T., Souza R.M.C.R. de, Chavent M., Lechevallier Y. (2006b): Adaptive Hausorff Distances and Dynamic Clustering of Symbolic Interval Data. Pattern Recognition Letters 27(3).10.1016/j.patrec.2005.08.014
    Search in Google Scholar
  13. Esposito F., Malerba D., Tamma V. (2000): Dissimilarity Measures for Symbolic Objects, In: Analysis of Symbolic Data: Exploratory Methods for Extracting Statistical Information from Complex Data, H.-H. Bock and E. Diday (Eds.), Berlin: Springer-Verlag: 165-185.
    Search in Google Scholar
  14. Fraser D.A.S. (1975): Non Parametric Methods in Statistics. Chapman and Hall.
    Search in Google Scholar
  15. Lerman I.C. (1972): Étude Distributionelle de Statistiques de Proximité entre Structures Algébriques Finies du Même Type: Apllication à la Classification Automatique. Cahiers du B.U.R.O., 19, Paris.
    Search in Google Scholar
  16. Lerman I.C. (1981): Classification et Analyse Ordinale des Données, Paris: Dunod.
    Search in Google Scholar
  17. Matusita K. (1951): On the theory of Statistical Decision Functions, Ann. Instit. Stat. Math. III: 1-30.
    Search in Google Scholar
  18. Nicolau F.C. (1983): Cluster Analysis and Distribution Function. Methods of Operations Research 45: 431-433.
    Search in Google Scholar
  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.
    Search in Google Scholar
  20. Nicolau F.C. (1983): Cluster Analysis and Distribution Function. Methods of Operations Research 45: 431-433.
    Search in Google Scholar
  21. Souza R.M.C.R. de, De Carvalho F.A.T. (2004): Clustering of interval data Based on City-Block distances, Pattern Recognition Letters 25: 353-365.10.1016/j.patrec.2003.10.016
    Search in Google Scholar
DOI: https://doi.org/10.2478/bile-2013-0015 | Journal eISSN: 2199-577X | Journal ISSN: 1896-3811
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Language: English
Page range: 27 - 38
Published on: Jun 5, 2013
Published by: Polish Biometric Society
In partnership with: Paradigm Publishing Services
Publication frequency: 2 issues per year
Keywords:
Ascendant Hierarchical Cluster Analysis,
Symbolic Data,
Interval Data,
Affinity Coefficient,
VL Methodology
Related subjects:
Life sciences,
Bioinformatics,
Life sciences, other,
Mathematics,
Probability and statistics,
Applied mathematics

© 2013 Áurea Sousa, Helena Bacelar-Nicolau, Fernando C. Nicolau, Osvaldo Silva, published by Polish Biometric Society
This work is licensed under the Creative Commons License.

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