Methoden vergelijken

	DBSCAN	K-means Clustering	Zelf-gesuperviseerd Leren
Vakgebied	Machine learning	Machine learning	Machine learning
Familie	Machine learning	Machine learning	Machine learning
Jaar van ontstaan	1996	1967 (formalized 1982)	2018–2020
Grondlegger	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. B.; Lloyd, S. P.	LeCun, Y. and community (formalized ~2018–2020)
Type	Density-based clustering algorithm	Partitional clustering	Representation learning paradigm
Oorspronkelijke bron	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.	LeCun, Y. & Misra, I. (2022). Self-supervised learning: The dark matter of intelligence. Meta AI Blog. https://ai.facebook.com/blog/self-supervised-learning-the-dark-matter-of-intelligence/
Aliassen	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	SSL, self-supervised pre-training, pretext-task learning, unsupervised representation learning
Verwant	3	4	3
Samenvatting	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.	Self-supervised learning (SSL) is a machine-learning paradigm that generates its own supervisory signal directly from unlabeled data by defining an auxiliary pretext task — such as predicting masked words, rotating images, or contrasting augmented views — and uses the learned representations as a powerful starting point for downstream tasks with minimal labeled examples.