Compara mètodes

	Ensemble HDBSCAN	K-means d'ensemble
Camp	Aprenentatge automàtic	Aprenentatge automàtic
Família	Machine learning	Machine learning
Any d'origen	2011–2017	2002
Autor original	Vega-Pons, S. & Ruiz-Shulcloper, J. (ensemble clustering framework); McInnes, L. et al. (HDBSCAN base)	Strehl, A. & Ghosh, J.
Tipus	Consensus clustering ensemble	Ensemble clustering (consensus aggregation of K-means partitions)
Font seminal	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.
Àlies	HDBSCAN ensemble clustering, consensus HDBSCAN, multi-run HDBSCAN, cluster ensemble HDBSCAN	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM
Relacionats	4	3
Resum	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.	Ensemble K-means runs K-means clustering many times under varied initializations, random seeds, or feature subsets, then aggregates the resulting partitions into a single consensus assignment. This approach reduces K-means' well-known sensitivity to initialization and produces more stable, reproducible clusters than any single run.