Cluster LOCO: Feature Importance For Interpreting Clusters
Clustering is widely used for exploratory analysis and scientific discovery, driving insights from market segmentation to biological data analysis, but its outputs can be difficult to interpret, audit, and reproduce as modern datasets become increasingly large and complex. Reliable use of clustering requires understanding which features drive the discovered structure, yet feature-level explanations for clustering remain scarce compared with methods in supervised learning. Furthermore, existing clustering feature importance scores are often tied to specific algorithms and data assumptions. To address these challenges, we propose Cluster LOCO (Leave-One-Covariate-Out), a family of model-agn...
| Authors | Claire M. He, Genevera I. Allen |
|---|---|
| Primary category | stat.ML |
| Published | 12 June 2026 |
| Last updated at source | 12 June 2026 |
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