Σύγκριση μεθόδων

	Συστάδα K-means (Ensemble K-means)	HDBSCAN	Ημι-επιβλεπόμενο HDBSCAN
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning	Machine learning
Έτος προέλευσης	2002	2013	2017–present
Δημιουργός	Strehl, A. & Ghosh, J.	Campello, R. J. G. B.; Moulavi, D.; Sander, J.	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors
Τύπος	Ensemble clustering (consensus aggregation of K-means partitions)	Hierarchical density-based clustering	Semi-supervised density-based clustering
Θεμελιώδης πηγή	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.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
Εναλλακτικές ονομασίες	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	HDBSCAN, Hierarchical DBSCAN, hierarchical density-based clustering, HDBSCAN*	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN
Συναφείς	3	3	6
Σύνοψη	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.	Semi-supervised HDBSCAN extends the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithm by incorporating partial supervision — such as must-link and cannot-link pairwise constraints or a small set of labeled examples — to guide the density-based cluster hierarchy toward cluster assignments that are consistent with available domain knowledge.