fuzzy clustering, distance metric, high-dimensional data, computational intelligence, membership function

The fuzzy clustering algorithm for high-dimensional data is proposed in this paper. An objective function which is insensitive to the “concentration of norms” phenomenon is also introduced. We recommend using a weighted parameter in the objective function. The proposed fuzzy clustering algorithm is compared with FCM in the experimental part. Dependence of the clustering algorithm’s results on the weighted parameter changes has also been investigated and tested.