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

• URL: http://arxiv.org/abs/2602.18348v1
• Date: Fri, 20 Feb 2026 17:01:25 GMT
• Title: Explaining AutoClustering: Uncovering Meta-Feature Contribution in AutoML for Clustering
• Authors: Matheus Camilo da Silva, Leonardo Arrighi, Ana Carolina Lorena, Sylvio Barbon Junior,
• Abstract summary: AutoClustering methods often leverage meta-learning over dataset meta-features.<n>This limits reliability, bias diagnostics, and efficient meta-feature engineering.<n>This study offers a practical foundation for increasing decision transparency in unsupervised learning automation.
• Score: 0.6487259764989486
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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.

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