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A hybrid algorithm for fuzzy clustering

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  • Z.H. Che

Abstract

The fuzzy C-means (FCM) algorithm is a commonly used fuzzy clustering method which conducts data clustering by randomly selecting initial centroids. With larger data size or attribute dimensions, clustering results may be affected and more repetitive computations are required. To compensate the effect of random initial centroids on results, this study proposed a hybrid algorithm - immune genetic annealing fuzzy C-means algorithm (IGAFA). This algorithm obtains the proper initial cluster centroids to improve clustering efficiency and then tests them through three data sets: Hamberman's survival, iris, and liver disorders, and compares the results with the executed results of genetic fuzzy C-means algorithm (GFA), immune fuzzy C-means algorithm (IFA), and annealing fuzzy C-means algorithm (AFA). The results suggest that IGAFA could achieve better clustering results. [Received: November 18, 2009; Accepted: July 19, 2010]

Suggested Citation

  • Z.H. Che, 2012. "A hybrid algorithm for fuzzy clustering," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 6(1), pages 50-67.
    • Handle: RePEc:ids:eujine:v:6:y:2012:i:1:p:50-67
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    References listed on IDEAS

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    1. K.Y. Chan & K.W. Chan & Glory T.Y. Pong & M.E. Aydin & T.C. Fogarty & S.H. Ling, 2009. "A statistics-based genetic algorithm for quality improvements of power supplies," European Journal of Industrial Engineering, Inderscience Enterprises Ltd, vol. 3(4), pages 468-492.
    2. D. Abramson, 1991. "Constructing School Timetables Using Simulated Annealing: Sequential and Parallel Algorithms," Management Science, INFORMS, vol. 37(1), pages 98-113, January.
    3. D Y Sha & Z H Che, 2006. "Supply chain network design: partner selection and production/distribution planning using a systematic model," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 57(1), pages 52-62, January.
    4. Loukil, Taicir & Teghem, Jacques & Fortemps, Philippe, 2007. "A multi-objective production scheduling case study solved by simulated annealing," European Journal of Operational Research, Elsevier, vol. 179(3), pages 709-722, June.
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