T - type of the points to clusterDBSCANClusterer instead@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:
Note: as DBSCAN is not a centroid-based clustering algorithm, the resulting
Cluster objects will have no defined center, i.e. Cluster.getCenter() will
return null.
| Constructor and Description |
|---|
DBSCANClusterer(double eps,
int minPts)
Deprecated.
Creates a new instance of a DBSCANClusterer.
|
| Modifier and Type | Method and Description |
|---|---|
List<Cluster<T>> |
cluster(Collection<T> points)
Deprecated.
Performs DBSCAN cluster analysis.
|
double |
getEps()
Deprecated.
Returns the maximum radius of the neighborhood to be considered.
|
int |
getMinPts()
Deprecated.
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 consideredminPts - minimum number of points needed for a clusterNotPositiveException - if eps < 0.0 or minPts < 0public double getEps()
public int getMinPts()
public List<Cluster<T>> cluster(Collection<T> points) throws NullArgumentException
Note: as DBSCAN is not a centroid-based clustering algorithm, the resulting
Cluster objects will have no defined center, i.e. Cluster.getCenter() will
return null.
points - the points to clusterNullArgumentException - if the data points are nullCopyright © 2003–2016 The Apache Software Foundation. All rights reserved.