Vertaile menetelmiä

	K-means-klusterointi	DBSCAN	t-SNE
Tieteenala	Koneoppiminen	Koneoppiminen	Koneoppiminen
Menetelmäperhe	Machine learning	Machine learning	Machine learning
Syntyvuosi	1967 (formalized 1982)	1996	2008
Kehittäjä	MacQueen, J. B.; Lloyd, S. P.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	van der Maaten, L. & Hinton, G.
Tyyppi	Partitional clustering	Density-based clustering algorithm	Nonlinear dimensionality reduction (manifold visualization)
Alkuperäislähde	Lloyd, S. P. (1982). Least squares quantization in PCM. IEEE Transactions on Information Theory, 28(2), 129–137.	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.	van der Maaten, L. & Hinton, G. (2008). Visualizing Data using t-SNE. Journal of Machine Learning Research, 9(86), 2579–2605.
Rinnakkaisnimet	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	t-SNE (Boyut İndirgeme / Görselleştirme), t-distributed stochastic neighbor embedding, tsne
Liittyvät	4	3	3
Tiivistelmä	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.	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.	t-SNE (t-Distributed Stochastic Neighbor Embedding) is a nonlinear dimensionality-reduction method introduced by Laurens van der Maaten and Geoffrey Hinton in 2008 that maps high-dimensional data into a 2D or 3D space for visualization. It preserves probabilistic local similarities, so points that are neighbours in the original space stay close together, revealing cluster structure and local neighbourhoods.