Hierarchical clustering with maximum density paths and mixture models

• URL: http://arxiv.org/abs/2503.15582v2
• Date: Wed, 21 May 2025 10:35:42 GMT
• Title: Hierarchical clustering with maximum density paths and mixture models
• Authors: Martin Ritzert, Polina Turishcheva, Laura Hansel, Paul Wollenhaupt, Marissa A. Weis, Alexander S. Ecker,
• Abstract summary: t-NEB is a probabilistically grounded hierarchical clustering method.<n>It yields state-of-the-art clustering performance on naturalistic high-dimensional data.
• Score: 44.443538161979056
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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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