Topic-driven clustering for document datasets

In this paper, we define the problem of topic-driven clustering, which organizes a document collection ac cording to a given set of topics. We propose three topic-driven schemes that consider the similarity be tween documents and topics and the relationship among documents themselves simultaneously. We present a comprehensive experimental evaluation of the proposed topic-driven schemes on five datasets. Our experimental results show that the proposed topic-driven schemes are efficient and effective with topic prototypes of different levels of specificity.