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Using SOM as a Tool for Automated Design of Clustering Systems Based on Fuzzy Predicates

  • Conference paper
Advances in Self-Organizing Maps

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 198))

Abstract

Clustering task is a never-ending research topic. New methods are permanently proposed. In particular, Fuzzy Logic and Self-organizing Maps and their mutual cooperation have demonstrated to be interesting paradigms. We propose a general approach to obtain membership functions for a ranked clustering system based on fuzzy predicates logical operations, considering Gaussian-shaped curves. We find membership functions parameters from trained Self-organizing Maps, which generalize the statistical characteristics of data. The system is self-configured and it has the advantages of other fuzzy approaches. Clustering quality is assessed by labeled data, which allow computing accuracy. The proposal must be tested with more real datasets, though the preliminary results obtained in well-known datasets suggest that it is a promising clustering scheme.

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References

  1. Pizzi, N.J., Pedrycz, W.: Classifying High-Dimensional Patterns Using a Fuzzy Logic Discriminant Network. Advances in Fuzzy Systems 2012, 7 (2012)

    Article  Google Scholar 

  2. Zadeh, L.A.: Fuzzy sets. Information and Control 8, 338–353 (1965)

    Article  MathSciNet  MATH  Google Scholar 

  3. Zadeh, L.A.: The Concept of a Linguistic Variable and its Application to Approximate Reasoning. Information Sciences 8, 199–249 (1975)

    Article  MathSciNet  MATH  Google Scholar 

  4. Thawonmas, R., Abe, S.: Function approximation based on fuzzy rules extracted from partitioned numerical data. Trans. Sys. Man Cyber. Part B 29, 525–534 (1999)

    Article  Google Scholar 

  5. Kahramanli, H., Allahverdi, N.: Rule extraction from trained adaptive neural networks using artificial immune systems. Expert Syst. Appl. 36, 1513–1522 (2009)

    Article  Google Scholar 

  6. Bortolan, G., Pedrycz, W.: An interactive framework for an analysis of ECG signals. Artif. Intell. Med. 24, 109–132 (2002)

    Article  Google Scholar 

  7. Nomura, T., Miyoshi, T.: An adaptive rule extraction with the fuzzy self-organizing map and a comparison with other methods. In: Proceedings of the 3rd International Symposium on Uncertainty Modelling and Analysis (1995)

    Google Scholar 

  8. Drobics, M., Bodenhofer, U., Winiwarter, W.: Mining Clusters and Corresponding Interpretable Descriptions - A Three-Stage Approach. Expert Systems: The Journal of Knowledge Engineering and Neural Networks 19, 224–234 (2002)

    Google Scholar 

  9. Malone, J., McGarry, K., Wermter, S., Bowerman, C.: Data mining using rule extraction from Kohonen Self-organising maps. Neural Computing and Applications 15, 9–17 (2006)

    Article  Google Scholar 

  10. Pal, N.R., Laha, A., Das, J.: Designing fuzzy rule based classifier using Self-organizing feature map for analysis of multispectral satellite images. International Journal of Remote Sensing 26, 2219–2240 (2009)

    Article  Google Scholar 

  11. Sorayya, M., Aishah, S., Sapiyan, B.M., Sharifah Mumtazah, S.A.: A Self organizing map (SOM) guided rule based system for freshwater tropical algal analysis and prediction. Scientific Research and Essays 6, 5279–5284 (2011)

    Google Scholar 

  12. Espin Andrade, R., Mazcorro Téllez, G., Fernández González, E., Marx-Gómez, J., Lecich, M.I.: Compensatory Logic: a fuzzy normative model for decision making. Revista Investigación Operacional 27, 178–193 (2006)

    Google Scholar 

  13. Dubois, H., Prade, D.: Fuzzy Sets and Systems: Theory and Applications. Academic Press Inc., New York (1980)

    MATH  Google Scholar 

  14. Pedrycz, W.: Fuzzy clustering with a knowledge-based guidance. Pattern Recognition Letters 25, 469–480 (2004)

    Article  Google Scholar 

  15. Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11, 586–600 (2000)

    Article  Google Scholar 

  16. Kiang, M.Y.: Extending the Kohonen self-organizing map networks for clustering analysis. Computational Statistics Data Analysis 38, 161–180 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  17. Bouchet, A., Pastore, J.I., Espín Andrade, R., Brun, M., Ballarin, V.: Arithmetic Mean Based Compensatory Fuzzy Logic. International Journal of Computational Intelligence and Applications 10, 231–243 (2011)

    Article  MATH  Google Scholar 

  18. Kohonen, T.: Self organized formation of topological correct feature maps. Biol. Cybernetics 43, 59–96 (1982)

    Article  MathSciNet  MATH  Google Scholar 

  19. Meschino, G.J., Passoni, L.I., Scandurra, A.G., Ballarin, V.L.: Representación automática pseudo color de imágenes médicas mediante Mapas Autoorganizados. In: Simposio Argentino de Informática y Salud, SIS 2006, Ciudad de Mendoza, Argentina (2006)

    Google Scholar 

  20. Vesanto, J.: Data exploration process based on the Self−organizing map (2002)

    Google Scholar 

  21. Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Nueva Jersey (1999)

    MATH  Google Scholar 

  22. Meschino, G.J., Passoni, L.I., Scandurra, A.G.: Análisis de Datos Multivariados de Pacientes Diabéticos Internados con Redes Neuronales Autoorganizadas. Investigación Operativa 24, 73–85 (2004)

    Google Scholar 

  23. Vesanto, J.: SOM-Based Data Visualization Methods (1999)

    Google Scholar 

  24. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31, 264–323 (1999)

    Article  Google Scholar 

  25. Ruspini, E.H.: A new approach to clustering. Information and Control 15, 22–32 (1969)

    Article  MATH  Google Scholar 

  26. Forti, A., Foresti, G.L.: Growing hierarchical tree SOM: an unsupervised neural network with dynamic topology. Neural Network 19, 1568–1580 (2006)

    Article  MATH  Google Scholar 

  27. Fisher, R.A.: The Use of Multiple Measurements in Taxonomic Problems. Annals of Eugenics 7, 179–188 (1936)

    Google Scholar 

  28. Cortez, P., Cerdeira, A., Almeida, F., Matos, T., Reis, J.: Modeling wine preferences by data mining from physicochemical properties. Decision Support Systems 47, 547–553 (2009)

    Article  Google Scholar 

  29. Kwan, R.K.-S., Evans, A.C., Pike, G.B.: MRI simulation-based evaluation of image-processing and classification methods. IEEE Transactions on Medical Imaging 18, 1085–1097 (1999)

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Facultad de Ingeniería, Universidad Nacional de Mar del Plata, Mar del Plata, Argentina

    Gustavo J. Meschino, Diego S. Comas, Virginia L. Ballarin, Adriana G. Scandurra & Lucía I. Passoni

  2. Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Buenos Aires, Argentina

    Diego S. Comas

Authors
  1. Gustavo J. Meschino
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  2. Diego S. Comas
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  3. Virginia L. Ballarin
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  4. Adriana G. Scandurra
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  5. Lucía I. Passoni
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Corresponding author

Correspondence to Gustavo J. Meschino .

Editor information

Editors and Affiliations

  1. , Department of Electrical Engineering, University of Chile, Av. Tupper 2007, Santiago, 8370451, Chile

    Pablo A. Estévez

  2. , Computational NeuroEngineering Lab., University of Florida, 216 Larsen Hall, Gainesville, 32611-6200, USA

    José C. Príncipe

  3. , Facultad de Ingeniería y, Universidad de los Andes, San Carlos de Apoquindo Nº 2200, Santiago, 8370451, Chile

    Pablo Zegers

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Meschino, G.J., Comas, D.S., Ballarin, V.L., Scandurra, A.G., Passoni, L.I. (2013). Using SOM as a Tool for Automated Design of Clustering Systems Based on Fuzzy Predicates. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_9

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  • DOI: https://doi.org/10.1007/978-3-642-35230-0_9

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35229-4

  • Online ISBN: 978-3-642-35230-0

  • eBook Packages: EngineeringEngineering (R0)

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