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

TL;DR

This paper investigates the explainability of meta-models in AutoClustering, revealing feature importance patterns and structural weaknesses to improve transparency and trust in AutoML for clustering.

Contribution

It provides a comprehensive review of meta-feature importance methods and applies explainability tools to enhance interpretability in AutoClustering systems.

Findings

Identifies key meta-features influencing clustering decisions

Highlights structural weaknesses in current meta-learning approaches

Offers guidance for designing more interpretable AutoML systems

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.