Bandingkan metode

	HDBSCAN Daring	DBSCAN	Pembelajaran Daring
Bidang	Pembelajaran Mesin	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning	Machine learning
Tahun asal	2015–2017	1996	1958–2000s
Pencetus	Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors)
Tipe	Incremental hierarchical density-based clustering	Density-based clustering algorithm	Learning paradigm (sequential model update)
Sumber perintis	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.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X. (1996). A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. Proceedings of the 2nd KDD, 226–231.	Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194.
Alias	incremental HDBSCAN, streaming HDBSCAN, online hierarchical density clustering, dynamic HDBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	incremental learning, sequential learning, streaming learning, online machine learning
Terkait	6	3	6
Ringkasan	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.	DBSCAN is a density-based clustering algorithm, introduced by Ester, Kriegel, Sander and Xu in 1996, that groups together points lying in dense regions and flags points in sparse regions as noise. It is effective on noisy data and on clusters of irregular, non-spherical shapes.	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.