Document clustering is of high importance for many natural language technologies. A wide range of computational traditional topic models, such as LDA (Latent Dirichlet Allocation) and its variants, have made great progress. However, traditional LDA-based clustering algorithms might not give good results due to such probabilistic models require prior distributions which are always difficult to define. In this paper, we propose a probabilistic model named tpLDA, which incorporates different levels of topic popularit…