NDLI: Weighted-covariance factor fuzzy c-means clustering

Content Provider	IEEE Xplore Digital Library
Author	Rammal, A. Perrin, E. Vrabie, V. Bertrand, I. Chabbert, B.
Copyright Year	2015
Description	Author affiliation: CReSTIC-Chalons, Univ. of Reims Champagne-Ardenne (URCA), Châlons-en-Champagne, France (Rammal, A.; Perrin, E.; Vrabie, V.) \|\| INRA-URCA, Reims, France (Bertrand, I.; Chabbert, B.)
Abstract	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.
Starting Page	144
Ending Page	149
File Size	1284811
Page Count	6
File Format	PDF
e-ISBN	9781479956807
DOI	10.1109/TAEECE.2015.7113616
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-04-29
Publisher Place	Lebanon
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Algorithm design and analysis FCM-CM algorithm Shape Classification of vegetal biomass FCM-SM algorithm Linear programming FCM-M algorithm Classification algorithms Biomass Fuzzy C-Means (FCM) clustering Covariance matrices GG-algorithm Covariance-based weight Clustering algorithms Mid-infrared (MIR) spectra GK-algorithm
Content Type	Text
Resource Type	Article

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