The intuitionistic fuzzy

c

-means clustering algorithm cannot discover the non-convex cluster structure.To alleviate this problem

an intuitionistic fuzzy

c

-means clustering algorithm based on kernelled distance is proposed.By defining the intuitionistic fuzzy Euclid distance

we map the sample to a high-dimension feature space.So the former features can be reflected thoroughly

which is helpful for clustering.Experiments executed on one artificial data sets and one UCI data sets demonstrate the performance of the proposed method.Compared with the five classical cluster algorithms

our method is of obvious effectiveness and superiority.