In this paper, we propose a factor weighted fuzzy c-means clustering algorithm. Based on the inverse of a covariance factor, which assesses the collinearity between the centers and samples, this factor takes also into account the compactness of the samples within clusters. The proposed clustering algorithm allows to classify spherical and non-spherical structural clusters, contrary to classical fuzzy c-means algorithm that is only adapted for spherical structural clusters. Compared with other algorithms designed for non-spherical structural clusters, such as Gustafson-Kessel, Gath-Geva or adaptive Mahalanobis distance-based fuzzy c-means clustering algorithms, the proposed algorithm gives better numerical results on artificial and real well known data sets. Moreover, this algorithm can be used for high dimensional data, contrary to other algorithms that require the computation of determinants of large matrices. Application on Mid-Infrared spectra acquired on maize root and aerial parts of Miscanthus for the classification of vegetal biomass shows that this algorithm can successfully be applied on high dimensional data.


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    Title :

    Weighted-covariance factor fuzzy C-means clustering


    Contributors:

    Publication date :

    2015-01-01


    Remarks:

    Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), 2015 Third International Conference . 2015; 3. International Conference on Technological Advances in Electrical, Electronics and Computer Engineering (TAEECE), Beyrouth, LBN, 2015-04-29-2015-05-01, 144 - 149



    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Classification :

    DDC:    006 / 629





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