Computationally effective algorithm to the ε-Insensitive fuzzy clustering

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 data. The ε-insensitive Fuzzy C-Means (εFCM) clustering algorithm is free of this disadvantage, but has a very high computational burden and requires a choice of the insensitivity parameter(s) ε. In this paper, a new computationally effective ε-insensitive fuzzy c-means clustering algorithm with automatic adjustment of the insensitivity parameter(s) is introduced. Performance of the new clustering algorithm is experimentally verified using synthetic data with outliers and overlapped groups of heavy-tailed data.