Methoden vergleichen

	Ensemble K-Means (Ensemble-K-Mittel)	HDBSCAN
Fachgebiet	Maschinelles Lernen	Maschinelles Lernen
Familie	Machine learning	Machine learning
Entstehungsjahr	2002	2013
Urheber	Strehl, A. & Ghosh, J.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.
Typ	Ensemble clustering (consensus aggregation of K-means partitions)	Hierarchical density-based clustering
Wegweisende Quelle	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	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.
Aliasnamen	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*
Verwandt	3	3
Zusammenfassung	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.	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.