# Which Clustering Do You Want? Inducing Your Ideal Clustering with Minimal Feedback

arXiv: https://arxiv.org/abs/1401.5389 · HTML: https://mlsanity.com/paper/1401.5389/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2014.

## Built on by (most cited first, 4 of 4)

- [Large Language Models Enable Few-Shot Clustering](https://mlsanity.com/paper/2307.00524/) (2023)
- [Personalized Clustering via Targeted Representation Learning](https://mlsanity.com/paper/2412.13690/) (2024)
- [ClusterFusion: Hybrid Clustering with Embedding Guidance and LLM Adaptation](https://mlsanity.com/paper/2512.04350/) (2025)
- [Oracle-guided Contrastive Clustering](https://mlsanity.com/paper/2211.00409/) (2022)

## Abstract (arXiv)

While traditional research on text clustering has largely focused on grouping documents by topic, it is conceivable that a user may want to cluster documents along other dimensions, such as the authors mood, gender, age, or sentiment. Without knowing the users intention, a clustering algorithm will only group documents along the most prominent dimension, which may not be the one the user desires. To address the problem of clustering documents along the user-desired dimension, previous work has focused on learning a similarity metric from data manually annotated with the users intention or having a human construct a feature space in an interactive manner during the clustering process. With the goal of reducing reliance on human knowledge for fine-tuning the similarity function or selecting the relevant features required by these approaches, we propose a novel active clustering algorithm, which allows a user to easily select the dimension along which she wants to cluster the documents by inspecting only a small number of words. We demonstrate the viability of our algorithm on a variety of commonly-used sentiment datasets.