Social tags are receiving growing interests in information retrieval. In music information retrieval previous research has demonstrated that tags can assist in music classification and clustering. This paper studies the problem of combining tags and audio contents for artistic style clustering. After studying the effectiveness of using tags and audio contents separately for clustering, this paper proposes a novel language model that makes use of both data sources. Experiments with various methods for combining feature sets demonstrate that tag features are more useful than audio content features for style clustering and that the proposed model can marginally improve clustering performance by combing tags and audio contents.