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A Fuzzy C-Medoids-Based CLARA Algorithm for Fast Image Segmentation

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  • Y V Ramana Rao
  • S Abirami

Abstract

This paper proposes a clustering algorithm, Fuzzy CLARA, which combines Fuzzy C-Medoids algorithm (FCMDD) with Clustering LARge Applications (CLARA) algorithm with an application of the proposed algorithm for fast image segmentation. CLARA finds wide applications in different areas of data mining and is known to reduce time complexity while dealing with large datasets. The performance of the fuzzy CLARA algorithm is compared with fuzzy c-medoids algorithm and its linearized low complexity version. The efficiency of the clustering algorithms is measured using the clustering validity index Xie-Beni. The findings of the study show that the fuzzy CLARA algorithm gives better results with respect to both time complexity and Xie-Beni index compared to Fuzzy c-medoids algorithm and its linearized low complexity version.

Suggested Citation

  • Y V Ramana Rao & S Abirami, 2012. "A Fuzzy C-Medoids-Based CLARA Algorithm for Fast Image Segmentation," The IUP Journal of Computer Sciences, IUP Publications, vol. 0(1), pages 7-16, January.
    • Handle: RePEc:icf:icfjcs:v:6:y:2012:i:1:p:7-16
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