Σύγκριση μεθόδων

	Ομαδοποίηση K-means	DBSCAN
Πεδίο	Μηχανική Μάθηση	Μηχανική Μάθηση
Οικογένεια	Machine learning	Machine learning
Έτος προέλευσης	1967 (formalized 1982)	1996
Δημιουργός	MacQueen, J. B.; Lloyd, S. P.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
Τύπος	Partitional clustering	Density-based clustering algorithm
Θεμελιώδης πηγή	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.
Εναλλακτικές ονομασίες	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
Συναφείς	4	3
Σύνοψη	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.