[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-81642-en":3,"doc-seo-81642-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},81642,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Towards Robust and Scalable Density-based Clustering via Graph Propagation","CluProp is a new framework for clustering in high-dimensional spaces where cluster densities vary widely. It reinterprets density-based clustering as label propagation over neighborhood graphs, formally linking density-based ideas with graph connectivity. The method reduces sensitivity to traditional global parameters by using a deterministic density-based propagation strategy for scalable neighborhood identification. It is distance-metric agnostic and demonstrates strong performance on large datasets, handling millions of points in minutes while consistently beating baseline methods in accuracy.","Towards Robust and Scalable Density-based Clustering via Graph Propagation  \nYingtao Zheng 1 Hugo Phibbs 1 Ninh Pham 2  \narXiv :2605 .00390v1 [ cs .LG] 1 May 2026  \nAbstract  \nWe present CluProp, a novel framework that reimagines varied-density clustering in highdimensional spaces as a label propagation process over neighborhood graphs. Our approach formally bridges the gap between density-based clustering and graph connectivity, leveraging efficient propagation mechanisms from network science to mitigate the parameter sensitivity inherent in traditional density-based methods. Specifically, we introduce a deterministic density-based propagation strategy to ensure scalable neighborhood identification. The framework is agnostic to the choice of distance metric and exhibits superior performance on large-scale data, processing millions of points in minutes while consistently outperforming existing baselines in accuracy.  \n1. Introduction  \nDensity-based clustering (Campello et al. , 2020) identifies clusters as dense regions separated by sparse areas, allowing it to detect arbitrarily shaped clusters and handle noise effectively. Unlike methods like kmeans (Lloyd, 1982), it is agnostic to the choice of distance metric, does not assume spherical clusters or require the number of clusters in advance, making it ideal for complex, real-world data.  \nRepresentative density-based clustering methods, such as DBSCAN (Ester et al. , 1996) and Density Peak Clustering (DPC) (Rodriguez & Laio, 2014), can be interpreted as label propagation mechanisms over a graph defined by local density relationships. In this view, clusters are seeded at high-density points, called core points in DBSCAN or density peaks in DPC, which act as the initial label sources. The labels are  \n1 School of Computer Science, University of Auckland, New Zealand. 2 Department of Mathematics and Computer Science, University of Southern Denmark, Denmark. Correspondence to: Ninh Pham \u003C[pham@imada.sdu.dk](pham@imada.sdu.dk) > .  \nPreprint. May 4, 2026 .  \nthen propagated from high-to-low density regions, following the structure of a neighborhood graph, which typically assigns remaining points to the label of its near neighbor with similar densities. This formulation naturally respects the underlying density landscape: labels do not cross low-density regions, allowing the algorithm to discover non-convex clusters and separate noisy or sparse areas. Thus, clustering emerges from how labels flow along high-density paths in the neighborhood graph.  \nConsidering each data point as a node in a graph, DBSCAN or DPC has two primary steps, including (1) constructing an ε-neighborhood or k-nearest neighbor (kNN) graph to discover density and neighborhood of each node, and (2) propagating cluster labels from dense nodes to sparse nodes.  \nThe first step is the main computational bottleneck as forming these neighborhood graphs requires a worstcase O (n2 ) time for a dataset of n points in high dimensions on popular metric distances (Matousek, 1994 ; Weber et al. , 1998) . Prior work has tackled this issue by leveraging approximate nearest neighbor search (ANNS) techniques, such as random projections (Schneider & Vlachos, 2017 ; Xu & Pham, 2024), hashing (Esfandiari et al. , 2021 ; Okkels et al. , 2025) or sampling (Viswanath & Babu, 2009 ; Jang & Jiang, 2019 ; Jiang et al. , 2020), to approximate the graph construction. Other work (de Berg et al. , 2019 ; Gan & Tao, 2017 ; Mai et al. , 2016 ; Amagata & Hara, 2021 ; Huang & Ma, 2023) follow the prune-and-bound strategies based on geometric properties on metric spaces to reduce the number of ε-neighborhood queries while forming clusters.  \nWhile density-based clustering and its approximate variants are effective for discovering arbitrarily shaped clusters, they struggle on datasets with highly varied densities due to their reliance on global parameters. DBSCAN requires a fixed neighborhood radius (ε) and minimum points (minPts), which makes","cbCaivndUr341C7e","https://ap.wps.com/l/cbCaivndUr341C7e","pdf",316819,1,19,"English","en",105,"# Introduction\n## Motivation: density-based clustering and label propagation\n## Computational bottlenecks in neighborhood graph construction\n## Limitations with highly varied densities\n## Prior work on heterogeneous-density clustering\n## Contribution: CluProp and density-aware propagation","[{\"question\":\"What problem does CluProp address in density-based clustering?\",\"answer\":\"CluProp targets the difficulty of clustering data with highly varied densities, where traditional density-based methods rely on global parameters and can miss or merge sparse and dense clusters.\"},{\"question\":\"How does CluProp connect density-based clustering with graph methods?\",\"answer\":\"CluProp reformulates density-based clustering as a label propagation process over neighborhood graphs, so cluster labels flow along high-density paths rather than crossing low-density regions.\"},{\"question\":\"Why is neighborhood graph construction a major computational challenge?\",\"answer\":\"Constructing ε-neighborhood or kNN graphs can require up to O(n^2) time in high dimensions on common metric distances, making it a bottleneck at 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problem does CluProp address in density-based clustering?","Question",{"text":74,"@type":75},"CluProp targets the difficulty of clustering data with highly varied densities, where traditional density-based methods rely on global parameters and can miss or merge sparse and dense clusters.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How does CluProp connect density-based clustering with graph methods?",{"text":79,"@type":75},"CluProp reformulates density-based clustering as a label propagation process over neighborhood graphs, so cluster labels flow along high-density paths rather than crossing low-density regions.",{"name":81,"@type":72,"acceptedAnswer":82},"Why is neighborhood graph construction a major computational challenge?",{"text":83,"@type":75},"Constructing ε-neighborhood or kNN graphs can require up to O(n^2) time in high dimensions on common metric distances, making it a bottleneck at 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