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[2602.18348] Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
Summary
This article explores the explainability of AutoClustering methods in AutoML, focusing on the contribution of dataset meta-features to algorithm selection and hyperparameter optimization.
Why It Matters
As AutoML systems become more prevalent, understanding their decision-making processes is crucial for improving reliability and transparency. This research addresses the need for explainability in unsupervised learning, which can enhance trust and facilitate better model design.
Key Takeaways
- AutoClustering automates unsupervised learning tasks but lacks transparency in decision-making.
- The study reviews 22 existing methods and organizes their meta-features into a taxonomy.
- Global and local explainability techniques are applied to assess feature importance.
- Findings reveal patterns in meta-feature relevance and weaknesses in current strategies.
- The research provides actionable insights for improving AutoML design and interpretability.
Computer Science > Machine Learning arXiv:2602.18348 (cs) [Submitted on 20 Feb 2026] Title:Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering Authors:Matheus Camilo da Silva, Leonardo Arrighi, Ana Carolina Lorena, Sylvio Barbon Junior View a PDF of the paper titled Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering, by Matheus Camilo da Silva and 2 other authors View PDF HTML (experimental) Abstract: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 exPlanati...
