Search for: [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."]

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