# Moving Past Single Metrics: Exploring Short-Text Clustering Across Multiple Resolutions

arXiv: https://arxiv.org/abs/2502.17020 · HTML: https://mlsanity.com/paper/2502.17020/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 32 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 5 of 5)

- [MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers](https://mlsanity.com/paper/2002.10957/) (2020)
- [An Alternative to EM for Gaussian Mixture Models: Batch and Stochastic Riemannian Optimization](https://mlsanity.com/paper/1706.03267/) (2017)
- [Comparing clusterings and numbers of clusters by aggregation of calibrated clustering validity indexes](https://mlsanity.com/paper/2002.01822/) (2020)
- [Cluster validation by measurement of clustering characteristics relevant to the user](https://mlsanity.com/paper/1703.09282/) (2017)
- [Human-interpretable clustering of short-text using large language models](https://mlsanity.com/paper/2405.07278/) (2024)

## Built on by (most cited first, 1 of 1)

- [Balancing Complexity and Informativeness in LLM-Based Clustering: Finding the Goldilocks Zone](https://mlsanity.com/paper/2504.04314/) (2025)

## Abstract (arXiv)

Cluster number is typically a parameter selected at the outset in clustering problems, and while impactful, the choice can often be difficult to justify. Inspired by bioinformatics, this study examines how the nature of clusters varies with cluster number, presenting a method for determining cluster robustness, and providing a systematic method for deciding on the cluster number. The study focuses specifically on short-text clustering, involving 30,000 political Twitter bios, where the sparse co-occurrence of words between texts makes finding meaningful clusters challenging. A metric of proportional stability is introduced to uncover the stability of specific clusters between cluster resolutions, and the results are visualised using Sankey diagrams to provide an interrogative tool for understanding the nature of the dataset. The visualisation provides an intuitive way to track cluster subdivision and reorganisation as cluster number increases, offering insights that static, single-resolution metrics cannot capture. The results show that instead of seeking a single 'optimal' solution, choosing a cluster number involves balancing informativeness and complexity.