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	Ensemble K-means	Zhlukovanie K-means	Polo-dohľadové HDBSCAN
Odbor	Strojové učenie	Strojové učenie	Strojové učenie
Rodina	Machine learning	Machine learning	Machine learning
Rok vzniku	2002	1967 (formalized 1982)	2017–present
Tvorca	Strehl, A. & Ghosh, J.	MacQueen, J. B.; Lloyd, S. P.	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors
Typ	Ensemble clustering (consensus aggregation of K-means partitions)	Partitional clustering	Semi-supervised density-based clustering
Pôvodný zdroj	Strehl, A. & Ghosh, J. (2002). Cluster ensembles — a knowledge reuse framework for combining multiple partitions. Journal of Machine Learning Research, 3, 583–617.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.
Ďalšie názvy	consensus K-means, K-means ensemble clustering, cluster ensemble with K-means, EKM	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN
Príbuzné	3	4	6
Zhrnutie	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.	K-means is a classic unsupervised partitional clustering algorithm that divides a dataset into K non-overlapping groups by iteratively assigning each observation to its nearest centroid and updating centroids as the mean of their assigned points. It is one of the most widely used exploratory tools in machine learning and data analysis.	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.