# Characterizing and comparing external measures for the assessment of cluster analysis and community detection

arXiv: https://arxiv.org/abs/2102.00708 · HTML: https://mlsanity.com/paper/2102.00708/

## Key facts

- Cited by 6 papers in the map (data through 13 Oct 2026).
- Builds on 8 of its 70 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [The Impact of Random Models on Clustering Similarity](https://mlsanity.com/paper/1701.06508/) (2017)
- [Adjusting for Chance Clustering Comparison Measures](https://mlsanity.com/paper/1512.01286/) (2015)
- [Element-centric clustering comparison unifies overlaps and hierarchy](https://mlsanity.com/paper/1706.06136/) (2017)
- [Evaluation of Community Detection Methods](https://mlsanity.com/paper/1807.01130/) (2018)
- [Evaluating accuracy of community detection using the relative normalized mutual information](https://mlsanity.com/paper/1501.03844/) (2015)
- [Improved mutual information measure for classification and community detection](https://mlsanity.com/paper/1907.12581/) (2019)
- [Generalized Measures for the Evaluation of Community Detection Methods](https://mlsanity.com/paper/1303.5441/) (2013)
- [Understanding partition comparison indices based on counting object pairs](https://mlsanity.com/paper/1901.01777/) (2019)

## Built on by (most cited first, 6 of 6)

- [Normalised clustering accuracy: An asymmetric external cluster validity measure](https://mlsanity.com/paper/2209.02935/) (2022)
- [On the Use of Relative Validity Indices for Comparing Clustering Approaches](https://mlsanity.com/paper/2404.10351/) (2024)
- [Benchmarking of Clustering Validity Measures Revisited](https://mlsanity.com/paper/2511.05983/) (2025)
- [A Personalized Video-Based Hand Taxonomy: Application for Individuals with Spinal Cord Injury](https://mlsanity.com/paper/2403.18094/) (2024)
- [Effectiveness of High-Dimensional Distance Metrics on Solar Flare Time Series](https://mlsanity.com/paper/2511.01873/) (2025)
- [Adjusted Similarity Measures and a Violation of Expectations](https://mlsanity.com/paper/2601.10641/) (2026)

## Abstract (arXiv)

In the context of cluster analysis and graph partitioning, many external evaluation measures have been proposed in the literature to compare two partitions of the same set. This makes the task of selecting the most appropriate measure for a given situation a challenge for the end user. However, this issue is overlooked in the literature. Researchers tend to follow tradition and use the standard measures of their field, although they often became standard only because previous researchers started consistently using them. In this work, we propose a new empirical evaluation framework to solve this issue, and help the end user selecting an appropriate measure for their application. For a collection of candidate measures, it first consists in describing their behavior by computing them for a generated dataset of partitions, obtained by applying a set of predefined parametric partition transformations. Second, our framework performs a regression analysis to characterize the measures in terms of how they are affected by these parameters and transformations. This allows both describing and comparing the measures. Our approach is not tied to any specific measure or application, so it can be applied to any situation. We illustrate its relevance by applying it to a selection of standard measures, and show how it can be put in practice through two concrete use cases.