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Enhanced Self Organized Dynamic Tree Neural Network

  • Juan F. De Paz
  • Sara Rodríguez
  • Ana Gil
  • Juan M. Corchado
  • Pastora Vega
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

Cluster analysis is a technique used in a variety of fields. There are currently various algorithms used for grouping elements that are based on different methods including partitional, hierarchical, density studies, probabilistic, etc. This article will present the ESODTNN neural network, an evolution of the SODTNN network, which facilitates the revision process by merging its operational process with dendrogram techniques, and enables the automatic detection of clusters in an increased number of situations.

Keywords

Clustering SOM hierarchical clustering PAM Dendrogram 

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References

  1. 1.
    Furao, S., Ogura, T., Hasegawa, O.: An enhanced self-organizing incremental neural network for online unsupervised learning. Neural Networks 20, 893–903 (2007)zbMATHCrossRefGoogle Scholar
  2. 2.
    Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics, 59–69 (1982)Google Scholar
  3. 3.
    Fritzke, B.: A growing neural gas network learns topologies. In: Tesauro, G., Touretzky, D.S., Leen, T.K. (eds.) Advances in Neural Information Processing Systems, vol. 7, pp. 625–632 (1995)Google Scholar
  4. 4.
    Martinetz, T., Schulten, K.: A neural-gas network learns topologies. Artificial Neural Networks 1, 397–402 (1991)Google Scholar
  5. 5.
    Shen, F.: An algorithm for incremental unsupervised learning and topology representation. Ph.D. thesis. Tokyo Institute of Technology (2006)Google Scholar
  6. 6.
    Saitou, N., Nie, M.: The neighbor-joining method: A new method for reconstructing phylogenetic trees. Mol. Biol. 4, 406–425 (1987)Google Scholar
  7. 7.
    Xu, L.: Bayesian Ying–Yang machine, clustering and number of clusters. Pattern Recognition Letters 18(11-13), 1167–1178 (1997)CrossRefGoogle Scholar
  8. 8.
    Kaufman, L., Rousseeuw, P.J.: Finding Groups in Data: An Introduction to Cluster Analysis. Wiley, New York (1990)Google Scholar
  9. 9.
    Corchado, J.M., De Paz, J.F., Rodríguez, S., Bajo, J.: Model of experts for decision support in the diagnosis of leukemia patients. Artificial Intelligence in Medicine 46(3), 179–200 (2009)CrossRefGoogle Scholar
  10. 10.
    Hartigan, J.A., Wong, M.A.: A K-means clustering algorithm. Applied Statistics 28, 100–108 (1979)zbMATHCrossRefGoogle Scholar
  11. 11.
    Campos, R., Ricardo, M.: A fast algorithm for computing minimum routing cost spanning trees 52(17), 3229–3247 (2008)Google Scholar
  12. 12.
    Bajo, J., De Paz, J.F., De Paz, Y., Corchado, J.M.: Integrating case-based planning and RPTW neural networks to construct an intelligent environment for health care. Expert Systems with Applications 36(3) (2009)Google Scholar
  13. 13.
    Carbó, J., Molina, J.M., Dávila, J.: Fuzzy Referral based Cooperation in Social Networks of Agents. AI Communications 18(1), 1–13 (2005)zbMATHMathSciNetGoogle Scholar
  14. 14.
    García, J., Berlanga, A., Molina, J.M., Casar, J.R.: Methods for Operations Planning in Airport Decision Support Systems. Applied Intelligence 22(3), 183–206 (2005)CrossRefGoogle Scholar
  15. 15.
    Pavón, J., Arroyo, M., Hassan, S., Sansores, C.: Agent-based modelling and simulation for the analysis of social patterns. Pattern Recognition Letters 29, 1039–1048 (2008)CrossRefGoogle Scholar
  16. 16.
    Pavón, J., Gómez, J., Fernández, A., Valencia, J.: Development of intelligent multi-sensor surveillance systems with agents Robotics and Autonomous Systems 55(12), 892–903 (2007)Google Scholar
  17. 17.
    Rung-Wei, P., Yuh-Yuan, G., Miin-Shen, Y.: A new clustering approach using data envelopment analysis. European Journal of Operational Research 199(1), 276–284 (2009)zbMATHCrossRefGoogle Scholar
  18. 18.
    Kerr, G., Ruskina, H.J., Cranea, M., Doolan, P.: Techniques for clustering gene expression data. Computers in Biology and Medicine 38(3), 283–293 (2008)CrossRefGoogle Scholar
  19. 19.
    De Paz, J.F., Rodríguez, S., Bajo, J., Corchado, J.M., López, V.: Self Organized Dynamic Tree Neural Network. In: Cabestany, J., Sandoval, F., Prieto, A., Corchado, J.M. (eds.) IWANN 2009. LNCS, vol. 5517, pp. 221–228. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  20. 20.
    Baruque, B., Corchado, E.: A weighted voting summarization of SOM ensembles. Data Mining and Knowledge DiscoveryGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Juan F. De Paz
    • 1
    • 2
  • Sara Rodríguez
    • 1
    • 2
  • Ana Gil
    • 1
    • 2
  • Juan M. Corchado
    • 1
    • 2
  • Pastora Vega
    • 1
    • 2
  1. 1.Departamento de Informática y AutomáticaUniversidad de SalamancaSalamancaEspaña
  2. 2.Department of Computer Science and AutomationUniversity of Salamanca Plaza de la Merced s/nSalamancaSpain

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