Abstract: In order to settle the matter that the outliers have an adverse impact on clustering results, which is caused by the restriction of traditional normalization conditions, an improved expert cluster weighting method based on fuzzy kernel clustering is proposed. By loosening the normalization constraints, the effects of outliers on the clustering results could be overcome. At the same time, considering the limitation of the traditional clustering standard based on the linear coupling of information entropy and consistency coefficient, a cluster weighting method based on deviation entropy is proposed. The expert weights' is determined based on the contribution of the expert clustering, which overcomes the deficiency of previous method. The example shows that the method is feasible and effective.

Key words: group decision-making, experts' weights, fuzzy kernel clustering, deviation entropy