विधियों की तुलना करें

	के-मीन्स क्लस्टरिंग	DBSCAN
क्षेत्र	मशीन अधिगम	मशीन अधिगम
परिवार	Machine learning	Machine learning
उद्भव वर्ष	1967 (formalized 1982)	1996
प्रवर्तक	MacQueen, J. B.; Lloyd, S. P.	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.
प्रकार	Partitional clustering	Density-based clustering algorithm
मौलिक स्रोत	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.
उपनाम	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering
संबंधित	4	3
सारांश	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.