방법 비교

	Online HDBSCAN	앙상블 HDBSCAN
분야	머신러닝	머신러닝
계열	Machine learning	Machine learning
기원 연도	2015–2017	2011–2017
창시자	Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.	Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)
유형	Incremental hierarchical density-based clustering	Consensus clustering ensemble
원전	Hassani, M., Seidl, T. (2017). Using internal evaluation measures to validate the quality of diverse stream clustering algorithms. Vietnam Journal of Computer Science, 4(3), 171–183.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
별칭	incremental HDBSCAN, streaming HDBSCAN, online hierarchical density clustering, dynamic HDBSCAN	HDBSCAN ensemble clustering, consensus HDBSCAN, multi-run HDBSCAN, cluster ensemble HDBSCAN
관련	6	4
요약	Online HDBSCAN extends the HDBSCAN hierarchical density-based clustering algorithm to incrementally process streaming or sequentially arriving data. Rather than rebuilding the full hierarchy from scratch with each new observation, it maintains and locally updates the mutual reachability graph, minimum spanning tree, condensed cluster tree, and stability-based cluster extraction, enabling continuous density-based clustering without full-dataset reprocessing.	Ensemble HDBSCAN runs HDBSCAN multiple times under different hyperparameter settings or data subsamples and combines the resulting partitions into a single stable consensus clustering. Because HDBSCAN is sensitive to its minimum cluster size and minimum samples parameters, pooling multiple runs greatly reduces sensitivity to any single configuration and yields more reproducible cluster assignments on noisy, high-dimensional data.