Hierarchical clustering with maximum density paths and mixture models

• URL: http://arxiv.org/abs/2503.15582v1
• Date: Wed, 19 Mar 2025 15:37:51 GMT
• Title: Hierarchical clustering with maximum density paths and mixture models
• Authors: Martin Ritzert, Polina Turishcheva, Laura Hansel, Paul Wollenhaupt, Marissa Weis, Alexander Ecker,
• Abstract summary: Hierarchical clustering is an effective and interpretable technique for analyzing structure in data.<n>It is particularly helpful in settings where the exact number of clusters is unknown, and provides a robust framework for exploring complex datasets.<n>Our method addresses this limitation by leveraging a two-stage approach, first employing a Gaussian or Student's t mixture model to overcluster the data, and then hierarchically merging clusters based on the induced density landscape.<n>This approach yields state-of-the-art clustering performance while also providing a meaningful hierarchy, making it a valuable tool for exploratory data analysis.
• Score: 39.42511559155036
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Hierarchical clustering is an effective and interpretable technique for analyzing structure in data, offering a nuanced understanding by revealing insights at multiple scales and resolutions. It is particularly helpful in settings where the exact number of clusters is unknown, and provides a robust framework for exploring complex datasets. Additionally, hierarchical clustering can uncover inner structures within clusters, capturing subtle relationships and nested patterns that may be obscured by traditional flat clustering methods. However, existing hierarchical clustering methods struggle with high-dimensional data, especially when there are no clear density gaps between modes. Our method addresses this limitation by leveraging a two-stage approach, first employing a Gaussian or Student's t mixture model to overcluster the data, and then hierarchically merging clusters based on the induced density landscape. This approach yields state-of-the-art clustering performance while also providing 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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