Outliers are data values that lie away from the general clusters of other data values. It may be that an outlier implies the most important feature of a dataset. In this paper, a new fuzzy kernel clustering algorithm is presented to locate the critical areas that are often represented by only a few outliers. Through mercer kernel functions, the data in the original space are firstly mapped to a high-dimensional feature space. Then a modified objective function for fuzzy clustering is introduced in the feature space. An additional weighting factor is assigned to each vector in the feature space, and the weight value is updated using the iterative functions derived from the objective function. The final weight of a datum represents a kind of representativeness of the corresponding datum. With these weights, the experts can identify the outliers easily. The simulations demonstrate the feasibility of this method.