T- type of the points to cluster
@Deprecated public class DBSCANClusterer<T extends Clusterable<T>> extends Object
The DBSCAN algorithm forms clusters based on the idea of density connectivity, i.e. a point p is density connected to another point q, if there exists a chain of points pi, with i = 1 .. n and p1 = p and pn = q, such that each pair <pi, pi+1> is directly density-reachable. A point q is directly density-reachable from point p if it is in the ε-neighborhood of this point.
Any point that is not density-reachable from a formed cluster is treated as noise, and will thus not be present in the result.
The algorithm requires two parameters:
|Constructor and Description|
Creates a new instance of a DBSCANClusterer.
|Modifier and Type||Method and Description|
Performs DBSCAN cluster analysis.
Returns the maximum radius of the neighborhood to be considered.
Returns the minimum number of points needed for a cluster.
public DBSCANClusterer(double eps, int minPts) throws NotPositiveException
eps- maximum radius of the neighborhood to be considered
minPts- minimum number of points needed for a cluster
eps < 0.0or
minPts < 0
public double getEps()
public int getMinPts()
public List<Cluster<T>> cluster(Collection<T> points) throws NullArgumentException
points- the points to cluster
NullArgumentException- if the data points are null
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