The possibilistic fuzzy clustering algorithm overcomes the problem of sensitivity to noises and coincident clusters

but it assumes the contribution of each sample is equal

which leads to strong impact from outliers or noises and too many iterations.For this reason

this paper proposes a novel faster possibilistic fuzzy clustering algorithm based on the sample-weighted idea.The results of the experiments on standard data sets and synthetic data sets show that the sample-weighted algorithm is more robust against noises and outliers and reduces the time complexity effectively

and can obtain good clustering accuracy at the same time.