arXiv Machine Learning June 15, 2026 By Claire M. He, Genevera I. Allen

Cluster LOCO: Feature Importance For Interpreting Clusters

Read the original on arXiv Machine Learning →

arXiv:2606. 14592v1 Announce Type: cross Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

computer-vision

arXiv Machine Learning

Jul 7

i-IF-Learn: Iterative Feature Selection and Unsupervised Learning for High-Dimensional Complex Data

arXiv:2603. 24025v2 Announce Type: replace Abstract: Unsupervised learning of high-dimensional data is challenging due to irrelevant or noisy features obscuring underlying structures.

By Chen Ma, Wanjie Wang, Shuhao Fan

rag

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Aug 7

Beyond Feature Importance: A Comparative Analysis of Pattern Detection Methods in Cluster Interpretation

arXiv:2608. 05880v1 Announce Type: cross Abstract: 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.

By Benjamin Connor, Anna Jurek-Loughrey, Lu Bai, Muhammad Fahim

arXiv Machine Learning

Jun 2

Cluster Analysis with Resampling for Validation and Exploration (CARVE)

arXiv:2606. 00327v1 Announce Type: cross Abstract: Clustering is widely used across the sciences as the foundation for downstream data-driven scientific discoveries.

By Kai R. Wycik, Tiffany M. Tang, Tarek M. Zikry, Genevera I. Allen

benchmarks

arXiv Machine Learning

Jul 17

Cross-Cluster Weighted Forests

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By Maya Ramchandran, Rajarshi Mukherjee, Giovanni Parmigiani

safety

arXiv AI

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You Only Train Once: Differentiable Subset Selection for Omics Data

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efficiency benchmarks safety

arXiv Machine Learning

Jun 11

Provable Recovery of Locally Important Signed Features and Interactions from Random Forest

arXiv:2512. 11081v2 Announce Type: replace-cross Abstract: Feature and Interaction Importance (FII) methods are essential in supervised learning for assessing the relevance of input variables and their interactions in complex prediction models.

By Kata Vuk, Nicolas Alexander Ihlo, Merle Behr

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