Skip to main content
SpringerLink
Log in
Book cover

International Conference on Intelligent Data Engineering and Automated Learning

IDEAL 2000: Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents pp 35–41Cite as

A NEW NONHIERARCHICAL CLUSTERING PROCEDURE FOR SYMBOLIC OBJECTS

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1983))

Abstract

A new nonhierarchical clustering procedure for symbolic objects is presented wherein during the first stage of the algorithm, the initial seed points are selected using the concept of farthest neighbours, and in suceeding stages the seed points are computed iteratively until the seed points get stabilised.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   84.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E. Diday and J.C. Simon, Clustering Analysis:Communication and Cybernetics, Vol 10, NewYork, Springer Verlag, 1976, pp. 47–92.

    Google Scholar 

  2. E. Diday, C. Hayashi, M. Jambu and N. Ohsumi, Eds, Recent developments in clustering and data analysis, NewYork: Academic, 1987.

    Google Scholar 

  3. H.H. Bock, Ed, Classification and related methods of data analysis, Amsterdam: NorthHolland, 1987.

    Google Scholar 

  4. A.K. Jain and R.C. Dubes, Algorithms for clustering data, Englewood Cliffs, NJ: Prentice Hall, 1988.

    MATH  Google Scholar 

  5. E. Diday, Ed., Data analysis, learning symbolic and numeric knowledge, Antibes, France: Nova Science Publishers, 1989.

    Google Scholar 

  6. R.O. Duda and P.E. Hart, Pattern classification and scene analysis, NewYork: Wiley Interscience, 1973.

    MATH  Google Scholar 

  7. K.C. Gowda and E. Diday, “Symbolic clustering using a new dissimilarity measure”, Pattern Recognition, Vol 24, No. 6, pp. 567–578, 1991.

    Article  Google Scholar 

  8. M. Ichino, “General metrics for mixed features-The cartesian space theory for pattern recognition”, in proc. IEEE Conf. Systems, Man and Cybernetics, Atlanta, GA, pp. 14–17, 1988.

    Google Scholar 

  9. M. Ichino and H. Yaguchi, “General Minkowsky metric for mixed feature type”, IEEE transactions on Systems, Man and Cybernetics, Vol 24, pp. 698–708, 1994.

    Article  MathSciNet  Google Scholar 

  10. K.C. Gowda and E. Diday, “Symbolic clustering using a new similarity measure”, IEEE transactions on Systems, Man and Cybernetics, Vol 22, No. 2, pp. 368–378, 1992.

    Article  Google Scholar 

  11. K.C. Gowda and T.V. Ravi,“Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity”, Pattern Recognition Letters 16 (1995), pp. 647–652.

    Article  Google Scholar 

  12. K.C. Gowda and T.V. Ravi, “Divisive clustering of symbolic objects using the concepts of both similarity and dissimilarity”, Pattern Recognition, Vol 28, No. 8, pp. 1277–1282, 1995.

    Article  Google Scholar 

  13. E. Diday, The symbolic approach in clustering, classification and related methods of vdata analysis, H.H. Bock, Ed. Amsterdam, The Netherlands: Elsevier, 1988.

    Google Scholar 

  14. D.H. Fisher and P. Langley, “Approaches to conceptual clustering”, in Proc. 9 th International Joint Conference on Artificial Intelligence, Los Angeles, CA, 1985, pp. 691–697.

    Google Scholar 

  15. R. Michalski, R.E. Stepp and E.Diday, “ A recent advance in data analysis: clustering objects into classes characterized by conjuctive concepts,” Progress in Pattern Recognition, Vol 1, L. Kanal and A. Rosenfeld, eds (1981).

    Google Scholar 

  16. R. Michalski and R.E. Stepp, “Automated construction of classifications: Conceptual clustering versus numerical taxonomy”, IEEE transactions Pattern Analysis and Machine Intelligence, PAMI-5, pp. 396–410, 1983

    Article  Google Scholar 

  17. Y. Cheng and K.S. Fu, “Conceptual clustering in knowledge organisation”, IEEE transactions Pattern Analysis and Machine Intelligence, PAMI-7, pp. 592–598, 1985.

    Google Scholar 

  18. D.H. Fisher, “Knowledge acquisition via incremental conceputal clustering”, Machine Learning, No. 2, pp. 103–138, 1987.

    Google Scholar 

  19. K.C. Gowda, “ A feature reduction and unsupervised classification algorithm for multispectral data”, Pattern Recognition, Vol 17, No. 6, pp 667–676, 1984.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. India Technology Centre, Upper Ground Floor, Unit I and II, Innovator Building International Technology Park, 560 066, Bangalore

    T.V. Ravi

  2. Department of computer science and engineering, S. J. College of engineering, 570 006, Mysore, India

    K. Chidananda Gowda

Authors
  1. T.V. Ravi
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. K. Chidananda Gowda
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Department of Computer Science and Engineering, The Chinese University of Hong Kong, Shatin, Hong Kong

    Kwong Sak Leung  & Lai-Wan Chan  & 

  2. Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong, Shatin, Hong Kong

    Helen Meng

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Ravi, T., Gowda, K.C. (2000). A NEW NONHIERARCHICAL CLUSTERING PROCEDURE FOR SYMBOLIC OBJECTS. In: Leung, K.S., Chan, LW., Meng, H. (eds) Intelligent Data Engineering and Automated Learning — IDEAL 2000. Data Mining, Financial Engineering, and Intelligent Agents. IDEAL 2000. Lecture Notes in Computer Science, vol 1983. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44491-2_6

Download citation

  • DOI: https://doi.org/10.1007/3-540-44491-2_6

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41450-6

  • Online ISBN: 978-3-540-44491-6

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation