# Validation of cluster analysis results on validation data: A systematic framework

arXiv: https://arxiv.org/abs/2103.01281 · HTML: https://mlsanity.com/paper/2103.01281/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Mar 2021.

## Built on by (most cited first, 11 of 11)

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- [On the role of benchmarking data sets and simulations in method comparison studies](https://mlsanity.com/paper/2208.01457/) (2022)
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- [Clustering with minimum spanning trees: How good can it be?](https://mlsanity.com/paper/2303.05679/) (2023)
- [Normalised clustering accuracy: An asymmetric external cluster validity measure](https://mlsanity.com/paper/2209.02935/) (2022)
- [Unveiling 3D Ocean Biogeochemical Provinces in the North Atlantic: A Systematic Comparison and Validation of Clustering Methods](https://mlsanity.com/paper/2504.18181/) (2025)
- [Cluster Analysis with Resampling for Validation and Exploration (CARVE)](https://mlsanity.com/paper/2606.00327/) (2026)
- [Selecting the number of clusters, clustering models, and algorithms. A unifying approach based on the quadratic discriminant score](https://mlsanity.com/paper/2111.02302/) (2021)
- [Quantitative Energy Prediction based on Carbon Emission Analysis by DPR Framework](https://mlsanity.com/paper/2309.01115/) (2023)
- [Cluster LOCO: Feature Importance For Interpreting Clusters](https://mlsanity.com/paper/2606.14592/) (2026)
- [From Unsupervised Subgroups to Hypothetical State-Intervention Policies: An Evaluation of Selected Subgrouping Methods in Observational Health Data](https://mlsanity.com/paper/2607.26521/) (2026)

## Abstract (arXiv)

Cluster analysis refers to a wide range of data analytic techniques for class discovery and is popular in many application fields. To judge the quality of a clustering result, different cluster validation procedures have been proposed in the literature. While there is extensive work on classical validation techniques, such as internal and external validation, less attention has been given to validating and replicating a clustering result using a validation dataset. Such a dataset may be part of the original dataset, which is separated before analysis begins, or it could be an independently collected dataset. We present a systematic structured framework for validating clustering results on validation data that includes most existing validation approaches. In particular, we review classical validation techniques such as internal and external validation, stability analysis, hypothesis testing, and visual validation, and show how they can be interpreted in terms of our framework. We precisely define and formalise different types of validation of clustering results on a validation dataset and explain how each type can be implemented in practice. Furthermore, we give examples of how clustering studies from the applied literature that used a validation dataset can be classified into the framework.