# Integrating Document Clustering and Topic Modeling

arXiv: https://arxiv.org/abs/1309.6874 · HTML: https://mlsanity.com/paper/1309.6874/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Sep 2013.

## Built on by (most cited first, 9 of 9)

- [Scholastic: Graphical Human-Al Collaboration for Inductive and Interpretive Text Analysis](https://mlsanity.com/paper/2208.06133/) (2022)
- [Topic Modelling on Consumer Financial Protection Bureau Data: An Approach Using BERT Based Embeddings](https://mlsanity.com/paper/2205.07259/) (2022)
- [Exploring the Power of Topic Modeling Techniques in Analyzing Customer Reviews: A Comparative Analysis](https://mlsanity.com/paper/2308.11520/) (2023)
- [Topic Modeling with Fine-tuning LLMs and Bag of Sentences](https://mlsanity.com/paper/2408.03099/) (2024)
- [Author Clustering and Topic Estimation for Short Texts](https://mlsanity.com/paper/2106.09533/) (2021)
- [Weakly Supervised Prototype Topic Model with Discriminative Seed Words: Modifying the Category Prior by Self-exploring Supervised Signals](https://mlsanity.com/paper/2112.03009/) (2021)
- [Shuffle & Divide: Contrastive Learning for Long Text](https://mlsanity.com/paper/2304.09374/) (2023)
- [Question-Answering Model for Schizophrenia Symptoms and Their Impact on Daily Life using Mental Health Forums Data](https://mlsanity.com/paper/2310.00448/) (2023)
- [Understanding Developer Pain Points in Federated Learning: Insights from Stack Overflow and GitHub](https://mlsanity.com/paper/2607.19621/) (2026)

## Abstract (arXiv)

Document clustering and topic modeling are two closely related tasks which can mutually benefit each other. Topic modeling can project documents into a topic space which facilitates effective document clustering. 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 clusters. In this paper, we propose a multi-grain clustering topic model (MGCTM) which integrates document clustering and topic modeling into a unified framework and jointly performs 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 collection and a topic model component used for mining multi-grain topics including local topics specific to each cluster and global topics shared across clusters.We employ variational inference to approximate the posterior of hidden variables and learn model parameters. Experiments on two datasets demonstrate the effectiveness of our model.