Bandingkan metode

	DBSCAN	Clustering K-means
Bidang	Pembelajaran Mesin	Pembelajaran Mesin
Keluarga	Machine learning	Machine learning
Tahun asal	1996	1967 (formalized 1982)
Pencetus	Ester, M., Kriegel, H.-P., Sander, J. & Xu, X.	MacQueen, J. B.; Lloyd, S. P.
Tipe	Density-based clustering algorithm	Partitional clustering
Sumber perintis	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.
Alias	DBSCAN Kümeleme, density-based clustering, density-based spatial clustering	k-means clustering, Lloyd's algorithm, k-means partitioning, hard k-means
Terkait	3	4
Ringkasan	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.