DBSCAN (Density-based spatial clustering of applications with noise) is used to cluster data where the shape of the clusters is not important; unlike in k-means, DBSCAN clusters do not need to be spherical.

DBSCAN has two hyperparameters, ε and minPt. DBSCAN starts by looking for data points that have at least minPt other data points within a radius ε. Such data points naturally bunch together to form the clusters DBSCAN discovers. It then goes on to add any remaining data points that are within distance ε of a cluster to that cluster; the attribution is random where a data point is a potential member of two or more clusters. Any data points that then remain are marked as unclassified.

Note that the number of clusters is not one of the hyperparameters that needs to be specified.

Density-based spatial clustering of applications with noise
has functional building block
has input data type
IDT_Vector of quantitative variables
has internal model
has output data type
has learning style
has parametricity
PRM_Nonparametric with hyperparameter(s)
has relevance
sometimes supports
mathematically similar to