Beyond Feature Importance: A Comparative Analysis of Pattern Detection Methods in Cluster Interpretation

Abstract:Interpreting clustering outcomes remains a fundamental challenge in data analysis, particularly in domains such as healthcare where meaningful patterns must be extracted from high-dimensional data. While numerous explainability techniques exist, they are primarily designed to assess feature importance or provide local instance-level explanations rather than to identify structured patterns present within clusters. This work presents a comparative evaluation of commonly used post-hoc analysis methods for pattern detection in clustering results. To enable controlled evaluation, we introduce a suite of synthetic datasets in which predefined patterns are systematically injected. Three widely used techniques are evaluated: a Random Forest surrogate model with permutation feature importance, LIME (Local Interpretable Model-agnostic Explanations), and principal component analysis. Results demonstrate that although each method can successfully recover relevant features, none consistently detects all injected pattern types. These findings high- light a critical gap between existing explainability tools and the requirements of pattern-level cluster interpretation, motivating the development of dedicated pattern detection methodologies.

6 pages. Accepted in 36th Irish Signals and Systems Conference (ISSC) 2026

Machine Learning (cs.LG); Artificial Intelligence (cs.AI)

arXiv:2608.05880 [cs.LG]

(or arXiv:2608.05880v1 [cs.LG] for this version)

https://doi.org/10.48550/arXiv.2608.05880 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

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