Machine learning
DBSCAN
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.
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Sources
- 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. link ↗
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Referenced by
Affinity PropagationBIRCHExplainable DBSCANExplainable K-MeansExponential Random Graph ModelGaussian Mixture ModelHDBSCANHierarchical ClusteringK-meansLocal Outlier FactorMean ShiftOnline DBSCANOnline HDBSCANOnline K-meansOPTICSRobust HDBSCANRobust k-meansSelf-supervised DBSCANSemi-supervised DBSCANSemi-supervised HDBSCANSemi-supervised K-meansSpectral ClusteringStochastic Block Model