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IFIP International Conference on Computer Science and its Applications

CIIA 2015: Computer Science and Its Applications pp 105–116Cite as

Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index

  • Conference paper

Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT,volume 456)

Abstract

In this paper, we propose a new clustering method based on the combination of K-harmonic means (KHM) clustering algorithm and cluster validity index for remotely sensed data clustering. The KHM is essentially insensitive to the initialization of the centers. In addition, cluster validity index is introduced to determine the optimal number of clusters in the data studied. Four cluster validity indices were compared in this work namely, DB index, XB index, PBMF index, WB-index and a new index has been deduced namely, WXI. The Experimental results and comparison with both K-means (KM) and fuzzy C-means (FCM) algorithms confirm the effectiveness of the proposed methodology.

Keywords

  • Clustering
  • KHM
  • Cluster validity indices
  • Remotely sensed data
  • K-means
  • FCM

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Author information

Authors and Affiliations

  1. Earth Observation Division, Centre of Space Techniques, Arzew, Algeria

    Habib Mahi & Nezha Farhi

  2. Kaouter LABED, Faculty of Mathematics and Computer Science Mohamed Boudiaf, University - USTOMB, Oran, Algeria

    Kaouter Labed

Authors
  1. Habib Mahi
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  2. Nezha Farhi
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  3. Kaouter Labed
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Corresponding author

Correspondence to Habib Mahi .

Editor information

Editors and Affiliations

  1. Tahar Moulay University, Saida, Algeria

    Abdelmalek Amine

  2. LIAS/ISAE-ENSMA, Chasseneuil, France

    Ladjel Bellatreche

  3. Sidi Bel Abbès University, Sidi Bel Abbès, Algeria

    Zakaria Elberrichi

  4. University of Vienna, Vienna, Austria

    Erich J. Neuhold

  5. Poznan University of Technology Inst. Computing Science, Poznan, Poland

    Robert Wrembel

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© 2015 IFIP International Federation for Information Processing

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Mahi, H., Farhi, N., Labed, K. (2015). Remotely Sensed Data Clustering Using K-Harmonic Means Algorithm and Cluster Validity Index. In: Amine, A., Bellatreche, L., Elberrichi, Z., Neuhold, E., Wrembel, R. (eds) Computer Science and Its Applications. CIIA 2015. IFIP Advances in Information and Communication Technology, vol 456. Springer, Cham. https://doi.org/10.1007/978-3-319-19578-0_9

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  • DOI: https://doi.org/10.1007/978-3-319-19578-0_9

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-19577-3

  • Online ISBN: 978-3-319-19578-0

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