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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2012
    • vol. 1, nº 7, december 2012
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    • UNIR REVISTAS
    • Revista IJIMAI
    • 2012
    • vol. 1, nº 7, december 2012
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    A fuzzy c-means bi-sonar-based Metaheuristic Optimization Algorithm

    Autor: 
    Khan, Koffka
    ;
    Sahai, Ashok
    Fecha: 
    12/2012
    Palabra clave: 
    fuzzy; clustering; bi-sonar; metaheuristic; optimization; IJIMAI
    Tipo de Ítem: 
    article
    URI: 
    https://reunir.unir.net/handle/123456789/9612
    DOI: 
    http://dx.doi.org/10.9781/ijimai.2012.173
    Dirección web: 
    https://www.ijimai.org/journal/node/367
    Open Access
    Resumen:
    Fuzzy clustering is an important problem which is the subject of active research in several real world applications. Fuzzy c-means (FCM) algorithm is one of the most popular fuzzy clustering techniques because it is efficient, straightforward, and easy to implement. Fuzzy clustering methods allow the objects to belong to several clusters simultaneously, with different degrees of membership. Objects on the boundaries between several classes are not forced to fully belong to one of the classes, but rather are assigned membership degrees between 0 and 1 indicating their partial membership. However FCM is sensitive to initialization and is easily trapped in local optima. Bi-sonar optimization (BSO) is a stochastic global Metaheuristic optimization tool and is a relatively new algorithm. In this paper a hybrid fuzzy clustering method FCB based on FCM and BSO is proposed which makes use of the merits of both algorithms. Experimental results show that this proposed method is efficient and reveals encouraging results.
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    • vol. 1, nº 7, december 2012

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