Credibilistic clustering algorithms via alternating cluster estimation

Credibilistic clustering is a new clustering method using the credibility measure in fuzzy clustering. Zhou et al. (2014) presented the clustering model of credibilistic clustering together with a credibilistic clustering algorithm for solving the optimization model. In this paper, a further investigation on credibilistic clustering is made. Within the solution architecture of alternating cluster estimation, a family of general credibilistic clustering algorithms are designed for solving the credibilistic clustering model. Moreover, a new credibilistic clustering algorithm is recommended for the real applications. Numerical examples based on randomly generated data sets and real data sets are presented to illustrate the performance and effectiveness of the credibilistic clustering algorithms from different aspects. Results comparing with the fuzzy $$c$$c-means algorithm and the possibilistic clustering algorithms show that the proposed credibilistic clustering algorithms can survive from the coincident problem and the noisy environments, and provide the clustering results with high overall accuracy.