A new credibilistic clustering algorithm

Abstract This paper focuses on credibilistic clustering approach. A data clustering method partitions unlabeled data sets into clusters and labels them for various goals such as computer vision and pattern recognition. There are different models for objective function-based fuzzy clustering such as Fuzzy C-Means (FCM), Possibilistic C-Mean (PCM) and their combinations. Credibilistic clustering is a new approach in this field. In this paper, a new credibilistic clustering model is introduced in which credibility measure is applied instead of possibility measure in possibilistic clustering. Also, in objective function, the separation of clusters is considered in addition to the compactness within clusters. The steps of clustering are designed based on this approach. Finally, the main issues about model are discussed, and the results of computational experiments are presented to show the efficiency of the proposed model.