提出一种基于密度路径的分层聚类方法，解决高维数据聚类难题。
Hierarchical clustering with maximum density paths and mixture models
- 通过过聚类估计密度，再找簇间最大密度路径
- 在高维自然数据上表现优于现有方法
- 适合探索性数据分析，结果层次清晰可解释
分层聚类是一种有效且可解释的数据结构分析方法，能在不预设聚类数量的情况下揭示多尺度信息，并捕捉嵌套模式和细微关系，这些常被平面聚类忽略。然而，现有方法在高维数据上表现不佳，尤其当不同模态间无明显密度间隙时。本文提出 t-NEB，一种基于概率框架的分层聚类方法，在自然高维数据上达到当前最优性能。t-NEB 包含三步：(1) 通过过聚类进行密度估计；(2) 找到簇间的最大密度路径；(3) 通过自底向上的聚类合并构建层次结构。t-NEB 使用参数化概率密度模型完成过聚类与聚类合并，兼具高聚类性能与有意义的层次结构，是探索性数据分析的有力工具。代码见 https://github.com/ecker-lab/tneb clustering。
原文摘要 · Abstract (English)
Hierarchical clustering is an effective, interpretable method for analyzing structure in data. It reveals insights at multiple scales without requiring a predefined number of clusters and captures nested patterns and subtle relationships, which are often missed by flat clustering approaches. However, existing hierarchical clustering methods struggle with high-dimensional data, especially when there are no clear density gaps between modes. In this work, we introduce t-NEB, a probabilistically grounded hierarchical clustering method, which yields state-of-the-art clustering performance on naturalistic high-dimensional data. t-NEB consists of three steps: (1) density estimation via overclustering; (2) finding maximum density paths between clusters; (3) creating a hierarchical structure via bottom-up cluster merging. t-NEB uses a probabilistic parametric density model for both overclustering and cluster merging, which yields both high clustering performance and a meaningful hierarchy, making it a valuable tool for exploratory data analysis. Code is available at https://github.com/ecker-lab/tneb clustering.
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