How do you design clustering systems for explainability in regulated industries?

Explainability is achieved using interpretable clustering features, centroid analysis, and feature attribution methods.

In regulated industries, clustering decisions must be explainable. This requires mapping clusters to human-understandable features, analyzing centroid composition, and providing feature contribution summaries. Techniques like SHAP or feature importance can be adapted to clustering by analyzing distance contributions. This ensures transparency in decision-making.