Machine learning
HDBSCAN
HDBSCAN（具有噪声应用的层次化基于密度的聚类算法）是 Campello、Moulavi 和 Sander 于 2013 年提出的一种基于密度的聚类算法。它通过构建跨越所有密度尺度的完整密度聚类层次结构，然后提取一个稳定的平坦分区来扩展 DBSCAN，使其能够有效处理不同区域密度差异显著的数据集。
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来源
- Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei et al. (Eds.), Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science, vol. 7819 (pp. 160–172). Springer, Berlin, Heidelberg. DOI: 10.1007/978-3-642-37456-2_14 ↗
- Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015). Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection. ACM Transactions on Knowledge Discovery from Data, 10(1), Article 5. DOI: 10.1145/2733381 ↗
- McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205. DOI: 10.21105/joss.00205 ↗
如何引用本页
ScholarGate. (2026, June 3). Hierarchical Density-Based Spatial Clustering of Applications with Noise. ScholarGate. https://scholargate.app/zh/machine-learning/hdbscan
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