The result of widely used k-means algorithm is a two-way decision result, namely each object either belongs to one cluster or not. The two-way decision method is difficult to apply to some situations with uncertainty. Therefore, a three-way decision clustering method was proposed to show the three relationships between an object and a cluster. That is, the object definitely belongs to the cluster, the object may belong to the cluster or the object does not belong to the cluster. Obviously, the two-way decision is a special case of the three-way decision. A new separation index and clustering validity index were defined from the perspective of two aspects, which were the compactness of cluster and the separation among clusters considering the nearest neighbors. Then, an automatic three-way decision clustering algorithm was put forward. The method provides a new way to solve the problem of automatically determining the number of clusters in the framework of k-means algorithm for the uncertain information. The preliminary comparison experimental results on the artificial and real UCI data sets show that the proposed method is effective.