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An Algorithm for Adaptive Clustering and Visualisation of Highdimensional Data Sets

  • F. Schwenker
  • H. A. Kestler
  • G. Palm
Conference paper
Part of the International Centre for Mechanical Sciences book series (CISM, volume 408)

Abstract

We describe an algorithm for exploratory data analysis which combines adaptive c-means clustering and multi-dimensional scaling (ACMDS). ACMDS is an algorithm for the online visualization of clustering processes and may be considered as an alternative approach to Kohonen’s self organizing feature map (SOM). Whereas SOM is a heuristic neural network algorithm, ACMDS is derived from multivariate statistical algorithms. The implications of ACMDS are illustrated through five different data sets.

Keywords

Sudden Cardiac Death Cluster Center Representation Center Projection Center Handwritten Digit 
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.

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References

  1. [Breithardt and Borggrefe, 1986]
    Breithardt, G. and Borggrefe, M. (1986). Pathophysiological mechanisms and clinical significance of ventricular late potentials. Eur Heart J, 7: 364–385.Google Scholar
  2. [Breithardt et al., 1991]
    Breithardt, G., Cain, M., El-Sherif, N., Flowers, N., Hombach, V., Janse, M., Simson, M., and Steinbeck, G. (1991). Standards for analysis of ventricular late potentials using high resolution or signal-averaged electrocardiography. Eur Heart J, 12: 473–80.Google Scholar
  3. [Gomes et al., 1989]
    Gomes, J., Winters, S., Martinson, M., Machac, J., Stewart, D., and Targonski, A. (1989). The prognostic significance of quantitative signal-averaged variables relative to clinical variables, site of myocardial infarction, ejection fraction and ventricular premature beats. JACC, 13: 377384.Google Scholar
  4. [Höher and Hombach, 1991a]
    Höher, M. and Hombach, V. (1991a). Ven- trikuläre Spätpotentiale — Teil I Grundlagen. Herz e4 Rhythmus, 3 (3): 1–7.Google Scholar
  5. [Höher and Hombach, 1991b]
    Höher, M. and Hombach, V. (1991b). Ventrikuläre Spätpotentiale — Teil II Klinische Aspekte. Herz ê4 Rhythmus, 3 (4): 8–14.Google Scholar
  6. [Jain and Dubes, 1988]
    Jain, A. and Dubes, R. (1988). Algorithms for Clustering Data. Prentice Hall, Englewood Cliffs, New Jersey.Google Scholar
  7. Kohonen, 1995] Kohonen, T. (1995). Self-Organizing Maps. Springer.Google Scholar
  8. Kreßel, 1991] Kreßel, U. (1991). The Impact of the Learning-Set Size in Handwritten-Digit Recognition. In Kohonen, T., editor, Artificial Neural Networks. ICANN-91, North-Holland.Google Scholar
  9. [Linde et al., 1980]
    Linde, Y., Buzo, A., and Gray, R. (1980). An algorithm for vector quantizer design. IEEE Transactions on Communications, 28 (1): 8495.CrossRefGoogle Scholar
  10. [Lloyd, 1982]
    Lloyd, S. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28 (2): 129–137.MathSciNetCrossRefMATHGoogle Scholar
  11. MacQueen, 1967] MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In L.M.LeCam and J.Neyman, editors, Proceedings of the Fifth Berkeley Symposium on Mathematical Statistics and Probability volume I, pages 281–297. Berkeley University of California Press.Google Scholar
  12. [Moody and Darken, 1989]
    Moody, J. and Darken, C. (1989). Fast learning in networks of locally-tuned processing units. Neural Computation, 1 (2): 281–294.Google Scholar
  13. [Ritter and Schulten, 1988]
    Ritter, H. and Schulten, K. (1988). Convergence properties of Kohonen’s topology converving maps:fluctuations,stability, and dimension selection. Biological Cybernetics, 60: 59–71.MathSciNetCrossRefMATHGoogle Scholar
  14. [Sammon, 1969]
    Sammon, J. (1969). A nonlinear mapping for data structure analysis. IEEE Transactions on Computers, C-18: 401–409.Google Scholar
  15. [Scott, 1992]
    Scott, D. (1992). Multivariate Density Estimation. John Wiley and Sons, New York.CrossRefMATHGoogle Scholar
  16. [Simson, 1981]
    Simson, M. (1981). Use of Signals in the Terminal QRS Complex to Identify Patients with Ventricular Tachycardia after Myocardial Infarction. Circulation, 64 (2): 235–242.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Wien 2000

Authors and Affiliations

  • F. Schwenker
    • 1
  • H. A. Kestler
    • 2
    • 3
  • G. Palm
    • 4
  1. 1.University of UlmUlmGermany
  2. 2.University of UlmUlmGermany
  3. 3.University Hospital UlmUlmGermany
  4. 4.University of UlmUlmGermany

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