# Interpretable Clustering: A Survey

arXiv: https://arxiv.org/abs/2409.00743 · HTML: https://mlsanity.com/paper/2409.00743/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- Builds on 37 of its 135 references that are in the map.
- First posted on arXiv 1 Sep 2024.

## Builds on (most cited first, 30 of 37)

- [Towards A Rigorous Science of Interpretable Machine Learning](https://mlsanity.com/paper/1702.08608/) (2017)
- [Explanation in Artificial Intelligence: Insights from the Social Sciences](https://mlsanity.com/paper/1706.07269/) (2017)
- [A Survey Of Methods For Explaining Black Box Models](https://mlsanity.com/paper/1802.01933/) (2018)
- [Explaining Explanations: An Overview of Interpretability of Machine Learning](https://mlsanity.com/paper/1806.00069/) (2018)
- [Interpretable machine learning: definitions, methods, and applications](https://mlsanity.com/paper/1901.04592/) (2019)
- [A Survey on Neural Network Interpretability](https://mlsanity.com/paper/2012.14261/) (2020)
- [A Survey on the Explainability of Supervised Machine Learning](https://mlsanity.com/paper/2011.07876/) (2020)
- [The Disagreement Problem in Explainable Machine Learning: A Practitioner's Perspective](https://mlsanity.com/paper/2202.01602/) (2022)
- [Benchmarking and Survey of Explanation Methods for Black Box Models](https://mlsanity.com/paper/2102.13076/) (2021)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [A Survey on Interpretable Reinforcement Learning](https://mlsanity.com/paper/2112.13112/) (2021)
- [Explainable k-Means and k-Medians Clustering](https://mlsanity.com/paper/2002.12538/) (2020)
- [A Survey on Explainable Anomaly Detection](https://mlsanity.com/paper/2210.06959/) (2022)
- [ExKMC: Expanding Explainable k-Means Clustering](https://mlsanity.com/paper/2006.02399/) (2020)
- [An Improved Approximation for k-median, and Positive Correlation in Budgeted Optimization](https://mlsanity.com/paper/1406.2951/) (2014)
- [On the price of explainability for some clustering problems](https://mlsanity.com/paper/2101.01576/) (2021)
- [Interpretable Deep Clustering for Tabular Data](https://mlsanity.com/paper/2306.04785/) (2023)
- [Nearly-Tight and Oblivious Algorithms for Explainable Clustering](https://mlsanity.com/paper/2106.16147/) (2021)
- [How to Find a Good Explanation for Clustering?](https://mlsanity.com/paper/2112.06580/) (2021)
- [Shallow decision trees for explainable k-means clustering](https://mlsanity.com/paper/2112.14718/) (2021)
- [Near-Optimal Explainable k-Means for All Dimensions](https://mlsanity.com/paper/2106.15566/) (2021)
- [Near-optimal Algorithms for Explainable k-Medians and k-Means](https://mlsanity.com/paper/2107.00798/) (2021)
- [Balancing the Tradeoff Between Clustering Value and Interpretability](https://mlsanity.com/paper/1912.07820/) (2019)
- [Almost Tight Approximation Algorithms for Explainable Clustering](https://mlsanity.com/paper/2107.00774/) (2021)
- [Neural Clustering based Visual Representation Learning](https://mlsanity.com/paper/2403.17409/) (2024)
- [Interpretable Clustering via Multi-Polytope Machines](https://mlsanity.com/paper/2112.05653/) (2021)
- [Interpretable Clustering via Optimal Trees](https://mlsanity.com/paper/1812.00539/) (2018)
- [Benchmarking Instance-Centric Counterfactual Algorithms for XAI: From White Box to Black Box](https://mlsanity.com/paper/2203.02399/) (2022)
- [Cluster Explanation via Polyhedral Descriptions](https://mlsanity.com/paper/2210.08798/) (2022)
- [Random Cuts are Optimal for Explainable k-Medians](https://mlsanity.com/paper/2304.09113/) (2023)

## Built on by (most cited first, 8 of 8)

- [A Survey on Interpretability in Visual Recognition](https://mlsanity.com/paper/2507.11099/) (2025)
- [NetBurst: Event-Centric Forecasting of Bursty, Intermittent Time Series](https://mlsanity.com/paper/2510.22397/) (2025)
- [MDL-GBG: A Non-parametric and Interpretable Granular-Ball Generation Method for Clustering](https://mlsanity.com/paper/2605.08759/) (2026)
- [Towards Explainable Deep Clustering for Time Series Data](https://mlsanity.com/paper/2507.20840/) (2025)
- [Interpretable clustering via optimal multi-way decision trees](https://mlsanity.com/paper/2602.13586/) (2026)
- [Interpretable Fair Clustering](https://mlsanity.com/paper/2511.21109/) (2025)
- [IPBC: An Interactive Projection-Based Framework for Human-in-the-Loop Semi-Supervised Clustering of High-Dimensional Data](https://mlsanity.com/paper/2601.18828/) (2026)
- [Minimum Description Length based Granular-Ball Tree Regularization for Spectral Clustering](https://mlsanity.com/paper/2605.22410/) (2026)

## Abstract (arXiv)

In recent years, much of the research on clustering algorithms has primarily focused on enhancing their accuracy and efficiency, frequently at the expense of interpretability. However, as these methods are increasingly being applied in high-stakes domains such as healthcare, finance, and autonomous systems, the need of transparent and interpretable clustering outcomes has become a critical concern. This is not only necessary for gaining user trust but also for satisfying the growing ethical and regulatory demands in these fields. Ensuring that decisions derived from clustering algorithms can be clearly understood and justified is now a fundamental requirement. To address this need, this paper provides a comprehensive and structured review of the current state of explainable clustering algorithms, identifying key criteria to distinguish between various methods. These insights can effectively assist researchers in making informed decisions about the most suitable explainable clustering methods for specific application contexts, while also promoting the development and adoption of clustering algorithms that are both efficient and transparent. For convenient access and reference, an open repository organizes representative and emerging interpretable clustering methods under the taxonomy proposed in this survey, available at https://hulianyu.xyz/Interpretable-Clustering-Repository