Explainable AI for Mixed Data Clustering

Clustering, an unsupervised machine learning approach, aims to find groups of similar instances. Mixed data clustering is of particular interest since real-life data often consists of diverse data types. The unsupervised nature of clustering emphasizes the need to understand the criteria for defining and distinguishing clusters. Current explainable AI (XAI) methods for clustering focus on intrinsically explainable clustering techniques, surrogate model-based explanations utilizing established XAI frameworks, and explanations generated from inter-instance distances. However, there exists a research gap in developing post-hoc methods that directly explain clusterings without resorting to surrogate models or requiring prior knowledge about the clustering algorithm. Addressing this gap, our work introduces a model-agnostic, entropy-based Feature Importance Score for continuous and discrete data, offering direct and comprehensible explanations by highlighting key features, deriving rules, and identifying cluster prototypes. The comparison with existing XAI frameworks like SHAP and ClAMP shows that we achieve similar fidelity and simplicity, proving that mixed data clusterings can be effectively explained solely from the distributions of the features and assigned clusters, making complex clusterings comprehensible to humans.