# Cluster LOCO: Feature Importance For Interpreting Clusters

arXiv: https://arxiv.org/abs/2606.14592 · HTML: https://mlsanity.com/paper/2606.14592/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 18 of its 70 references that are in the map.
- First posted on arXiv 12 Jun 2026.

## Builds on (most cited first, 18 of 18)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Distribution-Free Predictive Inference For Regression](https://mlsanity.com/paper/1604.04173/) (2016)
- [All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously](https://mlsanity.com/paper/1801.01489/) (2018)
- [The many Shapley values for model explanation](https://mlsanity.com/paper/1908.08474/) (2019)
- [Veridical Data Science](https://mlsanity.com/paper/1901.08152/) (2019)
- [Understanding Random Forests: From Theory to Practice](https://mlsanity.com/paper/1407.7502/) (2014)
- [From Clustering to Cluster Explanations via Neural Networks](https://mlsanity.com/paper/1906.07633/) (2019)
- [Feature Importance: A Closer Look at Shapley Values and LOCO](https://mlsanity.com/paper/2303.05981/) (2023)
- [Influential Feature PCA for high dimensional clustering](https://mlsanity.com/paper/1407.5241/) (2014)
- [Cohort Shapley value for algorithmic fairness](https://mlsanity.com/paper/2105.07168/) (2021)
- [Validation of cluster analysis results on validation data: A systematic framework](https://mlsanity.com/paper/2103.01281/) (2021)
- [Feature Selection for Huge Data via Minipatch Learning](https://mlsanity.com/paper/2010.08529/) (2020)
- [Interpretable Machine Learning for Discovery: Statistical Challenges & Opportunities](https://mlsanity.com/paper/2308.01475/) (2023)
- [MP-Boost: Minipatch Boosting via Adaptive Feature and Observation Sampling](https://mlsanity.com/paper/2011.07218/) (2020)
- [Are machine learning interpretations reliable? A stability study on global interpretations](https://mlsanity.com/paper/2505.15728/) (2025)
- [iLOCO: Distribution-Free Inference for Feature Interactions](https://mlsanity.com/paper/2502.06661/) (2025)
- [Fast and Interpretable Consensus Clustering via Minipatch Learning](https://mlsanity.com/paper/2110.02388/) (2021)
- [Cluster Analysis with Resampling for Validation and Exploration (CARVE)](https://mlsanity.com/paper/2606.00327/) (2026)

## Abstract (arXiv)

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-agnostic feature importance scores for clustering. Cluster LOCO is built on feature occlusion and clustering generalizability, defined as whether cluster labels learned on one subset of the data can be accurately predicted on held-out samples. For any chosen clustering algorithm, Cluster LOCO quantifies a feature's importance by measuring how much its removal degrades generalizability. We first introduce Cluster LOCO-Split, which relies on data splitting, and then extend it to Cluster LOCO-MP, a minipatch ensemble-based version designed for large-scale data. Across synthetic simulations and an application to cell-type discovery in single-cell transcriptomics, we show that Cluster LOCO more reliably recovers informative features than existing clustering feature importance methods.