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Fusion of Kohonen Maps Ranked by Cluster Validity Indexes

  • Leandro Antonio Pasa
  • José Alfredo F. Costa
  • Marcial Guerra de Medeiros
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8480)

Abstract

In this study, a new approach to Kohonen Self-Organizing Maps fusion is presented: the use of modified cluster validity indexes as a criterion for merging Kohonen Maps. Computational simulations were performed with traditional dataset from the UCI Machine Learning Repository, with variations in map size, number of subsets to be merged and the percentage of dataset bagging. The fusion results were compared with a regular single Kohonen Map. In some selected parameters, the proposed method achieves a better accuracy measure.

Keywords

Fusion Self Organizing Maps Validity Index 

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References

  1. 1.
    Dietterich, T.G.: Ensemble methods in machine learning. In: Kittler, J., Roli, F. (eds.) MCS 2000. LNCS, vol. 1857, pp. 1–15. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  2. 2.
    Hansen, L.K., Salamon, P.: Neural network ensembles. IEEE Transactions on Pattern Analysis and Machine Intelligence 12, 10 (1990)CrossRefGoogle Scholar
  3. 3.
    Perrone, M.P., Cooper, L.N.: When networks disagree: ensemble methods for hybrid neural networks. In: Neural Networks for Speech and Image Processing, pp. 126–142. Chapman and Hall (1993)Google Scholar
  4. 4.
    Kohonen, T.: Self-organized maps, 2nd edn. Springer, Berlin (1997)CrossRefGoogle Scholar
  5. 5.
    Breiman, L.: Bagging predictors. Machine Learning 24, 123–140 (1996)zbMATHMathSciNetGoogle Scholar
  6. 6.
    Zhou, Z.-H., Wu, J., Tang, W.: Ensembling neural networks: many could be better than all. Artificial Intelligence 137(1-2), 239–263 (2002)CrossRefzbMATHMathSciNetGoogle Scholar
  7. 7.
    Gonçalves, M.L., De Andrade Netto, M.L., Costa, J.A.F., Zullo, J.: Data clustering using self-organizing maps segmented by mathematic morphology and simplified cluster validity indexes: an application in remotely sensed images. In: International Joint Conference on Neural Networks, IJCNN 2006, pp. 4421–4428 (2006)Google Scholar
  8. 8.
    Georgakis, A., Li, H., Gordan, M.: An ensemble of SOM networks for document organization and retrieval. In: International Conference on Adaptive Knowledge Representation and Reasoning (2005)Google Scholar
  9. 9.
    Saavedra, C., Salas, R., Moreno, S., Allende, H.: Fusion of self organizing maps. In: Sandoval, F., Prieto, A.G., Cabestany, J., Graña, M. (eds.) IWANN 2007. LNCS, vol. 4507, pp. 227–234. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  10. 10.
    Corchado, E., Baruque, B.: WeVoS-ViSOM: an ensemble summarization algorithm for enhanced data visualization. Neurocomputing 75, 171–184 (2012)CrossRefGoogle Scholar
  11. 11.
    Borrajo, M.L., Baruque, B., Corchado, E., Bajo, J., Corchado, J.M.: Hybrid neural intelligent system to predict business failure in small-to-medium-size enterprises. International Journal of Neural Systems 21(04), 277–296 (2011)CrossRefGoogle Scholar
  12. 12.
    Jiang, Y., Zhi-Hua, Z.: SOM ensemble-based image segmentation. Neural Processing Letters 20(3), 171–178 (2004)CrossRefGoogle Scholar
  13. 13.
    Low, K.H., Wee, K.L., Marcelo, H.A.: An ensemble of cooperative extended Kohonen maps for complex robot motion tasks. Neural Computation 17, 1411–1445 (2005)CrossRefzbMATHGoogle Scholar
  14. 14.
    DeLooze, L.L.: Attack characterization and intrusion detection using an ensemble of self-organizing maps. In: 2006 IEEE Information Assurance Workshop, pp. 108–115 (2006)Google Scholar
  15. 15.
    Fustes, D., Dafonte, C., Arcay, B., Manteiga, M., Smith, K., Vallenari, A., Luri, X.: SOM ensemble for unsupervised outlier analysis. Application to outlier identification in the Gaia astronomical survey. Expert Systems with Applications 40(5), 1530–1541 (2013)CrossRefGoogle Scholar
  16. 16.
    Tsai, C.-F.: Combining cluster analysis with classifier ensembles to predict financial distress. Information Fusion 16, 46–58 (2014)CrossRefGoogle Scholar
  17. 17.
    Halkidi, M., Vazirgiannis, M.: A density-based cluster validity approach using multi-representatives. Pattern Recognition Letters 29, 773–786 (2008)CrossRefGoogle Scholar
  18. 18.
    Milligan, G.W., Cooper, M.C.: An examination of procedures for determining the number of clusters in a data set. Psychometrika 50, 159–179 (1985)CrossRefGoogle Scholar
  19. 19.
    Bezdek, J.C., Pal, N.R.: Some new indexes of cluster validity. IEEE Transactions on Systems, Man and Cybernetic. B 28, 301–315 (1998)CrossRefGoogle Scholar
  20. 20.
    Pakhira, M.K., Bandopadhyay, S., Maulik, U.: Validity index for crisp and fuzzy clusters. Pattern Recognition 37(3), 487–501 (2004)CrossRefzbMATHGoogle Scholar
  21. 21.
    Davies, D.L., Bouldin, D.W.: A cluster separation measure. IEEE Transactions on Pattern Analysis and Machine Intelligence PAMI-1(2), 224–227 (1979)CrossRefGoogle Scholar
  22. 22.
    Bache, K., Lichman, M.: Machine Learning Repository. University of California, Irvine, School of Information and Computer Sciences (2013), http://archive.ics.uci.edu/ml

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Leandro Antonio Pasa
    • 1
    • 2
  • José Alfredo F. Costa
    • 2
  • Marcial Guerra de Medeiros
    • 2
  1. 1.Federal Technological University of Paraná, UTFPRMedianeiraBrazil
  2. 2.Federal University of Rio Grande do Norte, UFRNNatalBrazil

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