This paper introduces WECM, a novel evidential and subspace clustering algorithm. It is based on the Evidential c-means, a variant of the k-means designed to produce a credal partition, allowing a better representation of the partial knowledge regarding the class membership of objects. The WECM algorithm integrates weights on features and clusters to enhance the clustering separability and interpretability. Experiments conducted on synthetic and real data show the positive effects of the weights on the clustering performances.