@misc{Łęski_Jacek_M._An,
 author={Łęski, Jacek M.},
 howpublished={online},
 publisher={Zielona Góra: Uniwersytet Zielonogórski},
 language={eng},
 abstract={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 [epsilon]-insensitive Fuzzy C-Means ([epsilon]FCM) 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.},
 type={artykuł},
 title={An [epsilon]-insensitive approach to fuzzy clustering},
 keywords={fuzzy clustering, fuzzy c-means, robust methods, fuzzy c-medians},
}