# To Cluster, or Not to Cluster: An Analysis of Clusterability Methods

arXiv: https://arxiv.org/abs/1808.08317 · HTML: https://mlsanity.com/paper/1808.08317/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Aug 2018.

## Built on by (most cited first, 17 of 17)

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- [UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusters](https://mlsanity.com/paper/2312.11323/) (2023)
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- [Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Data](https://mlsanity.com/paper/2409.12590/) (2024)
- [Dataset-Adaptive Dimensionality Reduction](https://mlsanity.com/paper/2507.11984/) (2025)
- [Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentences](https://mlsanity.com/paper/2110.00697/) (2021)
- [Multiclass histogram-based thresholding using kernel density estimation and scale-space representations](https://mlsanity.com/paper/2202.04785/) (2022)
- [IPD:An Incremental Prototype based DBSCAN for large-scale data with cluster representatives](https://mlsanity.com/paper/2202.07870/) (2022)
- [Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervision](https://mlsanity.com/paper/2208.10328/) (2022)
- [Clusterability test for categorical data](https://mlsanity.com/paper/2307.07346/) (2023)
- [Neighborhood Stability as a Measure of Nearest Neighbor Searchability](https://mlsanity.com/paper/2602.16673/) (2026)
- [TextClusterLab: An Integrated Framework for Reliable Text Clustering Studies](https://mlsanity.com/paper/2606.28328/) (2026)

## Abstract (arXiv)

Clustering is an essential data mining tool that aims to discover inherent cluster structure in data. For most applications, applying clustering is only appropriate when cluster structure is present. As such, the study of clusterability, which evaluates whether data possesses such structure, is an integral part of cluster analysis. However, methods for evaluating clusterability vary radically, making it challenging to select a suitable measure. In this paper, we perform an extensive comparison of measures of clusterability and provide guidelines that clustering users can reference to select suitable measures for their applications.