mlsanityData to 7 Oct 2026

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

arXiv 1808.08317 ↗First posted 24 Aug 2018stat.ML cs.LGcited by 17

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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.

Key facts. Cited by 17 papers in the map (data through 7 Oct 2026). First posted on arXiv 24 Aug 2018.

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Citing papers per year: 2021: 2; 2022: 7; 2023: 3; 2024: 2; 2025: 1; 2026: 2.

17 papers.

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  1. 2022How to Use K-means for Big Data Clustering?cited by 7
  2. 2022Sauron U-Net: Simple automated redundancy elimination in medical image segmentation via filter pruningcited by 5
  3. 2022Significance-Based Categorical Data Clusteringcited by 3
  4. 2021A Distance-based Separability Measure for Internal Cluster Validationcited by 2
  5. 2023UniForCE: The Unimodality Forest Method for Clustering and Estimation of the Number of Clusterscited by 2
  6. 2024Asynchronous Multi-Server Federated Learning for Geo-Distributed Clientscited by 2
  7. 2022How I learned to stop worrying and love the curse of dimensionality: an appraisal of cluster validation in high-dimensional spacescited by 1
  8. 2023DenMune: Density peak based clustering using mutual nearest neighborscited by 1
  9. 2024Hybrid Ensemble Deep Graph Temporal Clustering for Spatiotemporal Datacited by 1
  10. 2025Dataset-Adaptive Dimensionality Reductioncited by 1
  11. 2021Clustering and Network Analysis for the Embedding Spaces of Sentences and Sub-Sentencescited by 0
  12. 2022Multiclass histogram-based thresholding using kernel density estimation and scale-space representationscited by 0
5 more
  1. 2022IPD:An Incremental Prototype based DBSCAN for large-scale data with cluster representativescited by 0
  2. 2022Repurposing Knowledge Graph Embeddings for Triple Representation via Weak Supervisioncited by 0
  3. 2023Clusterability test for categorical datacited by 0
  4. 2026Neighborhood Stability as a Measure of Nearest Neighbor Searchabilitycited by 0
  5. 2026TextClusterLab: An Integrated Framework for Reliable Text Clustering Studiescited by 0