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Workshops on Applications of Evolutionary Computation

EvoWorkshops 2009: Applications of Evolutionary Computing pp 426–431Cite as

An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation

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Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5484))

Abstract

In this article a multiobjective technique using improved differential evolution for fuzzy clustering has been proposed that optimizes multiple validity measures simultaneously. The resultant set of near-pareto-optimal solutions contains a number of nondominated solutions, which the user can judge relatively and pick up the most promising one according to the problem requirements. Real-coded encoding of the cluster centres is used for this purpose. Results demonstrating the effectiveness of the proposed technique are provided for numeric remote sensing data described in terms of feature vectors. One satellite image has also been classified using the proposed technique to establish its efficiency.

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References

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Author information

Authors and Affiliations

  1. Department of Information Technology, Academy of Technology, Adisaptagram, 712121, West Bengal, India

    Indrajit Saha

  2. Department of Computer Science and Engineering, Jadavpur University, Jadavpur, 700032, West Bengal, India

    Ujjwal Maulik

  3. Machine Intelligence Unit, Indian Statistical Institute, Kolkata, 700108, West Bengal, India

    Sanghamitra Bandyopadhyay

Authors
  1. Indrajit Saha
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  2. Ujjwal Maulik
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  3. Sanghamitra Bandyopadhyay
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Editor information

Editors and Affiliations

  1. Department of Animal Production Epidemiology and Ecology, University of Torino, Via Leonardo da Vinci 44, 10095, Grugliasco, Italy

    Mario Giacobini

  2. Quinn School of Business, Belfield, University College Dublin, Dublin 4, Ireland

    Anthony Brabazon

  3. Department of Computer Engineering, University of Parma, Viale Usberti 181/a, 43100, Parma, Italy

    Stefano Cagnoni

  4. Dalla Molle Institute for Artificial Intelligence (IDSIA), Gallelria 2, 6928, Manno-Lugano, Switzerland

    Gianni A. Di Caro

  5. School of Engineering and Applied Science,Aston Triangle, Aston University, B4 7ET, Birmingham, UK

    Anikó Ekárt

  6. Complex Adaptive Systems Group, Instituto Tecnológico de Informática, Ciudad PolitŽcnica de la Innovacin, Edif. 8G Acceso B, 46022, Valencia, Spain

    Anna Isabel Esparcia-Alcázar

  7. Next Generation Itelligent Networks Research Center (nexGIN RC), National University of Computer and Emerging Sciences (NUCES), Islamabad, Pakistan

    Muddassar Farooq

  8. Faculty of Economics and Social Sciences, Helmut-Schmidt-University, Holstenhofweg 85, 22043, Hamburg, Germany

    Andreas Fink

  9. Department of Informatics Engineering, Polo II - Pinhal de Marrocos, University of Coimbra, 3030 - 290, Coimbra, Portugal

    Penousal Machado

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© 2009 Springer-Verlag Berlin Heidelberg

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Cite this paper

Saha, I., Maulik, U., Bandyopadhyay, S. (2009). An Improved Multi-objective Technique for Fuzzy Clustering with Application to IRS Image Segmentation. In: Giacobini, M., et al. Applications of Evolutionary Computing. EvoWorkshops 2009. Lecture Notes in Computer Science, vol 5484. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01129-0_48

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  • DOI: https://doi.org/10.1007/978-3-642-01129-0_48

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-01128-3

  • Online ISBN: 978-3-642-01129-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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