# Algorithm-Agnostic Explainability for Unsupervised Clustering

arXiv: https://arxiv.org/abs/2105.08053 · HTML: https://mlsanity.com/paper/2105.08053/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 50 references that are in the map.
- First posted on arXiv 17 May 2021.

## Builds on (most cited first, 6 of 6)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- ["Why Should I Trust You?": Explaining the Predictions of Any Classifier](https://mlsanity.com/paper/1602.04938/) (2016)
- [Interpretable Explanations of Black Boxes by Meaningful Perturbation](https://mlsanity.com/paper/1704.03296/) (2017)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [Interpretable Clustering via Optimal Trees](https://mlsanity.com/paper/1812.00539/) (2018)

## Built on by (most cited first, 4 of 4)

- [Counterfactual Explanations for Clustering Models](https://mlsanity.com/paper/2409.12632/) (2024)
- [Algorithm-Agnostic Interpretations for Clustering](https://mlsanity.com/paper/2209.10578/) (2022)
- [Position Paper: Bridging the Gap Between Machine Learning and Sensitivity Analysis](https://mlsanity.com/paper/2312.13234/) (2023)
- [Explainable Evidential Clustering](https://mlsanity.com/paper/2507.12192/) (2025)

## Abstract (arXiv)

Supervised machine learning explainability has developed rapidly in recent years. However, clustering explainability has lagged behind. Here, we demonstrate the first adaptation of model-agnostic explainability methods to explain unsupervised clustering. We present two novel "algorithm-agnostic" explainability methods - global permutation percent change (G2PC) and local perturbation percent change (L2PC) - that identify feature importance globally to a clustering algorithm and locally to the clustering of individual samples. The methods are (1) easy to implement and (2) broadly applicable across clustering algorithms, which could make them highly impactful. We demonstrate the utility of the methods for explaining five popular clustering methods on low-dimensional synthetic datasets and on high-dimensional functional network connectivity data extracted from a resting-state functional magnetic resonance imaging dataset of 151 individuals with schizophrenia and 160 controls. Our results are consistent with existing literature while also shedding new light on how changes in brain connectivity may lead to schizophrenia symptoms. We further compare the explanations from our methods to an interpretable classifier and find them to be highly similar. Our proposed methods robustly explain multiple clustering algorithms and could facilitate new insights into many applications. We hope this study will greatly accelerate the development of the field of clustering explainability.