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

• URL: http://arxiv.org/abs/2608.05880v1
• Date: Thu, 06 Aug 2026 11:00:50 GMT
• Title: Beyond Feature Importance: A Comparative Analysis of Pattern Detection Methods in Cluster Interpretation
• Abstract summary: This work presents a comparative evaluation of commonly used post-hoc analysis methods for pattern detection in clustering results.<n>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.<n>Results demonstrate that although each method can successfully recover relevant features, none consistently detects all injected pattern types.
• Score: 3.8070256855916456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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