Chapter 20. Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells Using FTIR Spectroscopy

  1. J. Valente de Oliveira3 and
  2. W. Pedrycz4,5
  1. Xiao-Ying Wang1,
  2. Jonathan M. Garibaldi1,
  3. Benjamin Bird2 and
  4. Mike W. George2

Published Online: 11 MAY 2007

DOI: 10.1002/9780470061190.ch20

Advances in Fuzzy Clustering and its Applications

Advances in Fuzzy Clustering and its Applications

How to Cite

Wang, X.-Y., Garibaldi, J. M., Bird, B. and George, M. W. (2007) Novel Developments in Fuzzy Clustering for the Classification of Cancerous Cells Using FTIR Spectroscopy, in Advances in Fuzzy Clustering and its Applications (eds J. Valente de Oliveira and W. Pedrycz), John Wiley & Sons, Ltd, Chichester, UK. doi: 10.1002/9780470061190.ch20

Editor Information

  1. 3

    The Ualg Informatics Lab, Faculty of Science and Technology, University of Algarve, Portugal

  2. 4

    Department of Electrical and Computer Engineering University of Alberta, Canada

  3. 5

    Systems Research Institute of the Polish Academy of Sciences Warsaw, Poland

Author Information

  1. 1

    School of Computer Science and Information Technology, University of Nottingham, UK

  2. 2

    School of Chemistry, University of Nottingham, UK

Publication History

  1. Published Online: 11 MAY 2007
  2. Published Print: 20 APR 2007

ISBN Information

Print ISBN: 9780470027608

Online ISBN: 9780470061190

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Keywords:

  • fuzzy clustering novel developments;
  • FTIR spectroscopic analysis;
  • fuzzy C-means (FCM) clustering;
  • clustering techniques;
  • K-means (KM) clustering - nonhierarchical clustering algorithm;
  • fuzzy k-means;
  • clustering validity;
  • SAFC algorithm

Summary

This chapter contains sections titled:

  • Introduction

  • Clustering Techniques

  • Cluster Validity

  • Simulated Annealing Fuzzy Clustering Algorithm

  • Automatic Cluster Merging Method

  • Conclusion

  • Acknowledgements

  • References