3. Rough-Fuzzy Clustering: Generalized cA-Means Algorithm

  1. Pradipta Maji1 and
  2. Sankar K. Pal2

Published Online: 17 FEB 2012

DOI: 10.1002/9781118119723.ch3

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging

How to Cite

Maji, P. and Pal, S. K. (2012) Rough-Fuzzy Clustering: Generalized cA-Means Algorithm, in Rough-Fuzzy Pattern Recognition: Applications in Bioinformatics and Medical Imaging, John Wiley & Sons, Inc., Hoboken, NJ, USA. doi: 10.1002/9781118119723.ch3

Author Information

  1. 1

    Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

  2. 2

    Center for Soft Computing Research, Indian Statistical Institute, Kolkata, India

Publication History

  1. Published Online: 17 FEB 2012
  2. Published Print: 27 JAN 2012

Book Series:

  1. Wiley Series on Bioinformatics: Computational Techniques and Engineering

Book Series Editors:

  1. Yi Pan and
  2. Albert Y. Zomaya

Series Editor Information

  1. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, India

ISBN Information

Print ISBN: 9781118004401

Online ISBN: 9781118119723

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Keywords:

  • fuzzy c-means (FCM) algorithm;
  • hard c-means (HCM) algorithm;
  • performance analysis;
  • rough c-means (RCM) algorithms;
  • rough-fuzzy clustering;
  • rough-fuzzy-possibilistic c-means (RFPCM) algorithm

Summary

Clustering techniques have been effectively applied to a wide range of engineering and scientific disciplines such as pattern recognition, biology, and remote sensing. A number of clustering algorithms have been proposed to suit different requirements. One of the widely used prototype-based partitional clustering algorithms is hard c-means (HCM). This chapter first briefly introduces the necessary notions of HCM, fuzzy c-means (FCM), and rough c-means (RCM) algorithms. It then describes the rough-fuzzy-possibilistic c-means (RFPCM) algorithm in detail on the basis of the theory of rough sets and FCM. The chapter also presents a mathematical analysis of the convergence property of the RFPCM algorithm. It establishes that the RFPCM algorithm is the generalization of existing c-means algorithms. The chapter reports several quantitative performance measures to evaluate the quality of different algorithms. Finally, it presents a few case studies and an extensive comparison with other methods such as crisp, fuzzy, possibilistic, and RCM.

Controlled Vocabulary Terms

fuzzy logic; pattern clustering; performance evaluation; rough set theory