The clustering criterion of the conventional Rough CMeans (RCM) algorithm is based on the hypothesis that the attributes involved in clustering are equally important. However, in the natural scenario clustering problems, the different attributes have different effects on the clustering results. To address this issue, we propose a weighted RCM by weighting clustering attributes. Specifically, in order to filter out discerning clustering attributes that have a crucial impact on the clustering results, the algorithm assigns different attributes to different attribute weights by introducing a weight matrix. The experimental results show that the proposed method is able to extract the attributes, which improves the clustering accuracy.