# Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering

arXiv: https://arxiv.org/abs/2602.18348 · HTML: https://mlsanity.com/paper/2602.18348/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 26 of its 86 references that are in the map.
- First posted on arXiv 20 Feb 2026.

## Builds on (most cited first, 26 of 26)

- [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)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI](https://mlsanity.com/paper/1910.10045/) (2019)
- [Meta-Learning in Neural Networks: A Survey](https://mlsanity.com/paper/2004.05439/) (2020)
- [OpenML: networked science in machine learning](https://mlsanity.com/paper/1407.7722/) (2014)
- [Meta-Learning: A Survey](https://mlsanity.com/paper/1810.03548/) (2018)
- [Explainable Artificial Intelligence (XAI) 2.0: A Manifesto of Open Challenges and Interdisciplinary Research Directions](https://mlsanity.com/paper/2310.19775/) (2023)
- [Tunability: Importance of Hyperparameters of Machine Learning Algorithms](https://mlsanity.com/paper/1802.09596/) (2018)
- [How Complex is your classification problem? A survey on measuring classification complexity](https://mlsanity.com/paper/1808.03591/) (2018)
- [Hyperparameter Importance Across Datasets](https://mlsanity.com/paper/1710.04725/) (2017)
- [Explaining Hyperparameter Optimization via Partial Dependence Plots](https://mlsanity.com/paper/2111.04820/) (2021)
- [Probabilistic Matrix Factorization for Automated Machine Learning](https://mlsanity.com/paper/1705.05355/) (2017)
- [Automating biomedical data science through tree-based pipeline optimization](https://mlsanity.com/paper/1601.07925/) (2016)
- [Are Cluster Validity Measures (In)valid?](https://mlsanity.com/paper/2208.01261/) (2022)
- [PED-ANOVA: Efficiently Quantifying Hyperparameter Importance in Arbitrary Subspaces](https://mlsanity.com/paper/2304.10255/) (2023)
- [Position: A Call to Action for a Human-Centered AutoML Paradigm](https://mlsanity.com/paper/2406.03348/) (2024)
- [Hyperparameter Importance Analysis for Multi-Objective AutoML](https://mlsanity.com/paper/2405.07640/) (2024)
- [Constraint-Based Clustering Selection](https://mlsanity.com/paper/1609.07272/) (2016)
- [Characterizing classification datasets: a study of meta-features for meta-learning](https://mlsanity.com/paper/1808.10406/) (2018)
- [Decision Predicate Graphs: Enhancing Interpretability in Tree Ensembles](https://mlsanity.com/paper/2404.02942/) (2024)
- [Hyperparameter Importance for Machine Learning Algorithms](https://mlsanity.com/paper/2201.05132/) (2022)
- [Automatic selection of clustering algorithms using supervised graph embedding](https://mlsanity.com/paper/2011.08225/) (2020)
- [Improving Accuracy of Interpretability Measures in Hyperparameter Optimization via Bayesian Algorithm Execution](https://mlsanity.com/paper/2206.05447/) (2022)
- [Problem-oriented AutoML in Clustering](https://mlsanity.com/paper/2409.16218/) (2024)
- [Extending Decision Predicate Graphs for Comprehensive Explanation of Isolation Forest](https://mlsanity.com/paper/2505.04019/) (2025)

## Abstract (arXiv)

AutoClustering methods aim to automate unsupervised learning tasks, including algorithm selection (AS), hyperparameter optimization (HPO), and pipeline synthesis (PS), by often leveraging meta-learning over dataset meta-features. While these systems often achieve strong performance, their recommendations are often difficult to justify: the influence of dataset meta-features on algorithm and hyperparameter choices is typically not exposed, limiting reliability, bias diagnostics, and efficient meta-feature engineering. This limits reliability and diagnostic insight for further improvements. In this work, we investigate the explainability of the meta-models in AutoClustering. We first review 22 existing methods and organize their meta-features into a structured taxonomy. We then apply a global explainability technique (i.e., Decision Predicate Graphs) to assess feature importance within meta-models from selected frameworks. Finally, we use local explainability tools such as SHAP (SHapley Additive exPlanations) to analyse specific clustering decisions. Our findings highlight consistent patterns in meta-feature relevance, identify structural weaknesses in current meta-learning strategies that can distort recommendations, and provide actionable guidance for more interpretable Automated Machine Learning (AutoML) design. This study therefore offers a practical foundation for increasing decision transparency in unsupervised learning automation.