手法を比較

	オンラインHDBSCAN	HDBSCAN	オンライン学習
分野	機械学習	機械学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2015–2017	2013	1958–2000s
提唱者	Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
種類	Incremental hierarchical density-based clustering	Hierarchical density-based clustering	Learning paradigm (sequential model update)
原典	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.	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.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
別名	incremental HDBSCAN, streaming HDBSCAN, online hierarchical density clustering, dynamic HDBSCAN	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	incremental learning, sequential learning, streaming learning, online machine learning
関連	6	3	6
概要	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.	HDBSCAN (Hierarchical Density-Based Spatial Clustering of Applications with Noise) is a density-based clustering algorithm introduced by Campello, Moulavi, and Sander in 2013. It extends DBSCAN by building a full hierarchy of density-based clusters across all density scales and then extracting a stable flat partition, making it robust to datasets where cluster densities vary substantially across regions.	Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight.