Geodesic
An ε-Insensitive Approach to Fuzzy Clustering
International Journal of Applied Mathematics and Computer Science, Tome 11 (2001) no. 4, pp. 993-1007.

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Fuzzy clustering can be helpful in finding natural vague boundaries in data. The fuzzy c-means method is one of the most popular clustering methods based on minimization of a criterion function. However, one of the greatest disadvantages of this method is its sensitivity to the presence of noise and outliers in the data. The present paper introduces a new varepsilon-insensitive Fuzzy C-Means (varepsilonFCM) clustering algorithm. As a special case, this algorithm includes the well-known Fuzzy C-Medians method (FCMED). The performance of the new clustering algorithm is experimentally compared with the Fuzzy C-Means (FCM) method using synthetic data with outliers and heavy-tailed, overlapped groups of the data.
Keywords: fuzzy clustering, fuzzy c-means, robust methods, varepsilon-insensitivity, fuzzy c-medians
Mots-clés : programowanie, metoda grupowania 
@article{IJAMCS_2001_11_4_a12,
     author = {{\L}\k{e}ski, J.},
     title = {An {\ensuremath{\varepsilon}-Insensitive} {Approach} to {Fuzzy} {Clustering}},
     journal = {International Journal of Applied Mathematics and Computer Science},
     pages = {993--1007},
     publisher = {mathdoc},
     volume = {11},
     number = {4},
     year = {2001},
     language = {en},
     url = {http://geodesic.mathdoc.fr/item/IJAMCS_2001_11_4_a12/}
}
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Łęski, J. An ε-Insensitive Approach to Fuzzy Clustering. International Journal of Applied Mathematics and Computer Science, Tome 11 (2001) no. 4, pp. 993-1007. http://geodesic.mathdoc.fr/item/IJAMCS_2001_11_4_a12/