Machine learningMachine learning
Ensemble HDBSCAN
Ensemble HDBSCAN 通过在不同的超参数设置或数据子样本下多次运行 HDBSCAN，并将所得的聚类结果合并为单一的稳定共识聚类。由于 HDBSCAN 对其最小簇大小 (minimum cluster size) 和最小样本数 (minimum samples) 参数敏感，因此合并多次运行的结果可以大大降低对单一配置的敏感性，并在嘈杂、高维数据上产生更具可复现性的聚类分配。
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来源
- 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 ↗
- Vega-Pons, S., & Ruiz-Shulcloper, J. (2011). A survey of clustering ensemble methods. International Journal of Pattern Recognition and Artificial Intelligence, 25(03), 337–372. DOI: 10.1142/S0218001411008683 ↗
如何引用本页
ScholarGate. (2026, June 3). Ensemble Hierarchical Density-Based Spatial Clustering of Applications with Noise. ScholarGate. https://scholargate.app/zh/machine-learning/ensemble-hdbscan
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