Integrating document clustering and topic modeling

Document clustering and topic modeling are two closely related tasks which can mutu- ally benefit each other. Topic modeling can project documents into a topic space which facilitates effective document cluster- ing. Cluster labels discovered by document clustering can be incorporated into topic models to extract local topics specific to each cluster and global topics shared by all clus- ters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which inte- grates document clustering and topic model- ing into a unified framework and jointly per- forms the two tasks to achieve the overall best performance. Our model tightly couples two components: a mixture component used for discovering latent groups in document col- lection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global top- ics shared across clusters. We employ varia- tional inference to approximate the posterior of hidden variables and learn model param- eters. Experiments on two datasets demon- strate the effectiveness of our model.