Fuzzy C-means algorithm is a fuzzy partitional clustering algorithm. However, accuracy and easy to implement have converted this algorithm to the focus of research, and sensitivity to noisy data is an important and challenging issue in the algorithm, so that in recent years, many studies have been done to improve it. In this paper, a clustering algorithm named Fuzzy VIKOR C-means presented that by utilizing the extended VIKOR method based on targeted displacements in the centroids of the clusters seek to benefit from the flexibility property. Moreover, this algorithm also, considering Dunn’s index, means, and density measures as profit criteria, and DB index and the entropy measures as cost criteria, can reduce the sensitivity to noisy data and can enhance performance and quality of clusters. According to the simulation results and comparison with some recent well-known methods, this approach has an effective role in improving the assessment criteria.
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