Vertaile menetelmiä

	Semi-supervised HDBSCAN	DBSCAN	K-means-klusterointi
Tieteenala	Koneoppiminen	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning
Syntyvuosi	2017–present	1996	1967 (formalized 1982)
Kehittäjä	McInnes, L.; Healy, J. (base HDBSCAN); semi-supervised extensions by various authors	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. B.; Lloyd, S. P.
Tyyppi	Semi-supervised density-based clustering	Density-based clustering algorithm	Partitional clustering
Alkuperäislähde	McInnes, L., Healy, J., & Astels, S. (2017). hdbscan: Hierarchical density based clustering. Journal of Open Source Software, 2(11), 205.	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.	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.
Rinnakkaisnimet	Constrained HDBSCAN, Semi-supervised hierarchical density clustering, HDBSCAN with partial labels, SS-HDBSCAN	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Liittyvät	6	3	4
Tiivistelmä	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.	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.	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.