Persistent Multiscale Density-based Clustering

• URL: http://arxiv.org/abs/2512.16558v1
• Date: Thu, 18 Dec 2025 14:01:35 GMT
• Title: Persistent Multiscale Density-based Clustering
• Authors: Daniël Bot, Leland McInnes, Jan Aerts,
• Abstract summary: Persistent Leaves Spatial Clustering for Applications with Noise (PLSCAN)<n>PLSCAN efficiently identifies all minimum cluster sizes for which HDBSCAN* produces stable (leaf) clusters.<n>We compare PLSCAN's performance to HDBSCAN* on several real-world datasets.
• Score: 0.515435457943463
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Clustering is a cornerstone of modern data analysis. Detecting clusters in exploratory data analyses (EDA) requires algorithms that make few assumptions about the data. Density-based clustering algorithms are particularly well-suited for EDA because they describe high-density regions, assuming only that a density exists. Applying density-based clustering algorithms in practice, however, requires selecting appropriate hyperparameters, which is difficult without prior knowledge of the data distribution. For example, DBSCAN requires selecting a density threshold, and HDBSCAN* relies on a minimum cluster size parameter. In this work, we propose Persistent Leaves Spatial Clustering for Applications with Noise (PLSCAN). This novel density-based clustering algorithm efficiently identifies all minimum cluster sizes for which HDBSCAN* produces stable (leaf) clusters. PLSCAN applies scale-space clustering principles and is equivalent to persistent homology on a novel metric space. We compare its performance to HDBSCAN* on several real-world datasets, demonstrating that it achieves a higher average ARI and is less sensitive to changes in the number of mutual reachability neighbours. Additionally, we compare PLSCAN's computational costs to k-Means, demonstrating competitive run-times on low-dimensional datasets. At higher dimensions, run times scale more similarly to HDBSCAN*.

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