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This study evaluates the effectiveness of three popular post-hoc analysis methods鈥擱andom Forest surrogate models with permutation feature importance, LIME, and principal component analysis鈥攊n detecting structured patterns within clustering results. By utilizing synthetic datasets with predefined patterns, the authors reveal that while each method identifies relevant features, none consistently captures all types of injected patterns. This highlights a significant shortcoming in current explainability techniques, underscoring the need for specialized methodologies tailored for pattern detection in clustering interpretation.
None of the popular explainability methods can reliably detect all types of patterns in clustering results, revealing a critical gap in current analytical tools.
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.
