能解释聚类结果依赖哪些特征
Cluster LOCO 是一种模型无关的聚类特征重要性打分方法，基于特征遮挡和聚类泛化性。它通过衡量移除某个特征后聚类标签在留出样本上的预测准确度下降来量化特征重要性。研究提出了基于数据划分的Cluster LOCO-Split和面向大规模数据的Cluster LOCO-MP（minipatch集成版）。在合成数据和单细胞转录组学的细胞类型发现任务中，Cluster LOCO比现有方法更可靠地恢复有信息量的特征。
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-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.