Uporedite metode

	Онлајн HDBSCAN	DBSCAN	Ensemble HDBSCAN	HDBSCAN
Oblast	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje	Mašinsko učenje
Porodica	Machine learning	Machine learning	Machine learning	Machine learning
Godina nastanka	2015–2017	1996	2011–2017	2013
Tvorac	Campello, R. J. G. B. et al. (base); incremental extensions by Hassani, M. et al.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Tip	Incremental hierarchical density-based clustering	Density-based clustering algorithm	Consensus clustering ensemble	Hierarchical density-based clustering
Temeljni izvor	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.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	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.
Drugi nazivi	incremental HDBSCAN, streaming HDBSCAN, online hierarchical density clustering, dynamic HDBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	HDBSCAN ensemble clustering, consensus HDBSCAN, multi-run HDBSCAN, cluster ensemble HDBSCAN	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
Srodne	6	3	4	3
Sažetak	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.	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.	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.