Cluster LOCO: Feature Importance For Interpreting Clusters

• URL: http://arxiv.org/abs/2606.14592v1
• Date: Fri, 12 Jun 2026 16:10:26 GMT
• Title: Cluster LOCO: Feature Importance For Interpreting Clusters
• Abstract summary: We present Cluster LOCO, a family of model-agnostic feature importance scores for clustering.<n>For any chosen clustering algorithm, Cluster LOCO quantifies a feature's importance by measuring how much its removal degrades generalizability.<n>We show that Cluster LOCO more reliably recovers informative features than existing clustering feature importance methods.
• Score: 5.6721325104114655
• License: http://creativecommons.org/licenses/by/4.0/
• 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. 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.

Related papers

• Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data [63.62853416081748]
  WISE is a framework that unifies representation, feature weighting, clustering, and interpretation.<n>It produces faithful, human-interpretable explanations grounded in the same primitives that drive clustering.
  arXiv  Detail & Related papers  (2026-04-07T13:18:31Z)
• Robust Categorical Data Clustering Guided by Multi-Granular Competitive Learning [47.32771052588132]
  The nested granular cluster effect is prevalent in the implicit discrete distance space of categorical data.<n>We propose a Multi-Granular Competitiveization Learning algorithm to allow potential clusters to interactively tune themselves.<n>It is shown that the proposed MGCPL-guided Categorical Data Clustering approach is competent in exploring the nested distribution of multi-granular clusters.
  arXiv  Detail & Related papers  (2026-01-23T06:33:08Z)
• ESMC: MLLM-Based Embedding Selection for Explainable Multiple Clustering [79.69917150582633]
  Multi-modal large language models (MLLMs) can be leveraged to achieve user-driven clustering.<n>Our method first discovers that MLLMs' hidden states of text tokens are strongly related to the corresponding features.<n>We also employ a lightweight clustering head augmented with pseudo-label learning, significantly enhancing clustering accuracy.
  arXiv  Detail & Related papers  (2025-11-30T04:36:51Z)
• Cluster-Level Sparse Multi-Instance Learning for Whole-Slide Images [9.658549716966176]
  Cluster-level Sparse MIL (csMIL) is a novel framework that integrates global-local instance clustering, within-cluster attention, and cluster-level sparsity induction.<n>Our csMIL first performs global clustering across all bags to establish $K$ cluster centers, followed by local clustering within each bag to assign cluster labels.<n>This approach enhances robustness to noisy instances, improves interpretability by identifying critical regions, and reduces computational complexity.
  arXiv  Detail & Related papers  (2025-09-14T01:50:51Z)
• Categorical Data Clustering via Value Order Estimated Distance Metric Learning [53.28598689867732]
  This paper introduces a novel order distance metric learning approach to intuitively represent categorical attribute values.<n>A new joint learning paradigm is developed to alternatively perform clustering and order distance metric learning.<n>The proposed method achieves superior clustering accuracy on categorical and mixed datasets.
  arXiv  Detail & Related papers  (2024-11-19T08:23:25Z)
• Large Language Models Enable Few-Shot Clustering [88.06276828752553]
  We show that large language models can amplify an expert's guidance to enable query-efficient, few-shot semi-supervised text clustering. We find incorporating LLMs in the first two stages can routinely provide significant improvements in cluster quality.
  arXiv  Detail & Related papers  (2023-07-02T09:17:11Z)
• Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [85.51611950757643]
  We propose IAC (Instance-Adaptive Clustering), the first algorithm whose performance matches the instance-specific lower bounds both in expectation and with high probability.<n>IAC maintains an overall computational complexity of $ mathcalO(n, textpolylog(n) $, making it scalable and practical for large-scale problems.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• A Generalized Framework for Predictive Clustering and Optimization [18.06697544912383]
  Clustering is a powerful and extensively used data science tool. In this article, we define a generalized optimization framework for predictive clustering. We also present a joint optimization strategy that exploits mixed-integer linear programming (MILP) for global optimization.
  arXiv  Detail & Related papers  (2023-05-07T19:56:51Z)
• Oracle-guided Contrastive Clustering [28.066047266687058]
  Oracle-guided Contrastive Clustering(OCC) is proposed to cluster by interactively making pairwise same-cluster" queries to oracles with distinctive demands. To the best of our knowledge, it is the first deep framework to perform personalized clustering.
  arXiv  Detail & Related papers  (2022-11-01T12:05:12Z)
• Enhancing cluster analysis via topological manifold learning [0.3823356975862006]
  We show that inferring the topological structure of a dataset before clustering can considerably enhance cluster detection. We combine manifold learning method UMAP for inferring the topological structure with density-based clustering method DBSCAN.
  arXiv  Detail & Related papers  (2022-07-01T15:53:39Z)
• You Never Cluster Alone [150.94921340034688]
  We extend the mainstream contrastive learning paradigm to a cluster-level scheme, where all the data subjected to the same cluster contribute to a unified representation. We define a set of categorical variables as clustering assignment confidence, which links the instance-level learning track with the cluster-level one. By reparametrizing the assignment variables, TCC is trained end-to-end, requiring no alternating steps.
  arXiv  Detail & Related papers  (2021-06-03T14:59:59Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.