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 ε-insensitive Fuzzy C-Means (εFCM) 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.
@article{bwmeta1.element.bwnjournal-article-amcv11i4p993bwm,
author = {\L \k eski, Jacek},
title = {An $\epsilon$-insensitive approach to fuzzy clustering},
journal = {International Journal of Applied Mathematics and Computer Science},
volume = {11},
year = {2001},
pages = {993-1007},
zbl = {1004.94043},
language = {en},
url = {http://dml.mathdoc.fr/item/bwmeta1.element.bwnjournal-article-amcv11i4p993bwm}
}
Łęski, Jacek. An ε-insensitive approach to fuzzy clustering. International Journal of Applied Mathematics and Computer Science, Tome 11 (2001) pp. 993-1007. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-amcv11i4p993bwm/
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