Self-Organizing Maps in the Design and Processing of Granular Information

  • Andrzej Bargiela
  • Witold Pedrycz
Part of the The Springer International Series in Engineering and Computer Science book series (SECS, volume 717)


In this chapter, we concentrate on a granular data analysis, especially studying ways of information granulation. We show how information granules are constructed by a designer/user via a visual inspection of self-organizing maps (SOMs). SOMs are commonly used neural network architectures realizing a paradigm of unsupervised learning. The crux of the approach proposed here lies in the following
  • a high level of interaction with user — it is worth stressing that the constructs (information granules) are delineated by a human on a basis of visualization of highly dimensional data,

  • a solid support of the development of information granules cast in the framework of sets and fuzzy sets.


Homogeneous Region Neighbor Function Information Granule Right Ventricular Hypertrophy Original Feature Space 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Bargiela, A., Pedrycz, W. (2001), Classification and clustering of granular data using SOM, IFSA-NAFIPS 2001, Vancouver (BC), July 2001, 1696–1701.Google Scholar
  2. Bortolan, G., Willems, J.L. (1994), Diagnostic ECG classification based on neural networks, Journal of Electrocardiology, 26, 75–79.Google Scholar
  3. Briand, L.C., S. Morasca, V.R. Basili (1996), Property-based software engineering measurements, IEEE Trans, on Software Engineering, 22, 68–86.CrossRefGoogle Scholar
  4. Chidamber, S.R., CF. Kemerer (1994) A Metrics suite for object-oriented design, IEEE Transactions on Software Engineering, 20(6).Google Scholar
  5. Fenton, N.E., S.L. Pfleeger (1997), Software Metrics: A Rigorous and Practical Approach, PWS, London.Google Scholar
  6. Kohonen, T.(1982), Self-organized formation of topologically correct feature maps, Biological Cybernetics, 43.Google Scholar
  7. Kohonen, T. (1995), Self-organizing Maps, Springer Verlag, Berlin.CrossRefGoogle Scholar
  8. Kohonen, T., S. Kaski, K. Lagus, T. Honkela (1996), Very large two-level SOM for the browsing of newsgroups, In: Proc of the Int Conf on Artificial Neural Networks, Bochum, Germany.Google Scholar
  9. Li, W., S. Henry (1993) Object oriented metrics that predict maintainability, Journal of Systems and Software, 23(2) Google Scholar
  10. Oja, E., Kaski S. (eds) (1999), Kohonen Maps, Elsevier, Amsterdam.MATHGoogle Scholar
  11. Silipo, R. Bortolan, G., Marchesi, C. (1999), Design of hybrid architectures based on neural classifier and RBF pre-processing for ECG analysis, Int. J. of Approximate Reasoning 21, 177–196.MATHCrossRefGoogle Scholar
  12. Willems, J.L., Lesaffre, E., Pardaens, J. (1987), Comparison of the classification ability of the electrocardiogram and vectorcardiogram, American J. Cardiology, 59, 119–124.CrossRefGoogle Scholar
  13. Weyuker, E.J. (1988) Evaluating software complexity measures, IEEE Transactions on Software Engineering, 14(9).Google Scholar
  14. Zuse, H. (1985) A Framework of Software Measurement, de Gruyter, Berlin.Google Scholar

Copyright information

© Springer Science+Business Media New York 2003

Authors and Affiliations

  • Andrzej Bargiela
    • 1
  • Witold Pedrycz
    • 2
  1. 1.The Nottingham Trent UniversityNottinghamUK
  2. 2.University of AlbertaEdmontonCanada

Personalised recommendations