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java.lang.Objectorg.apache.commons.math3.stat.clustering.DBSCANClusterer<T>
T - type of the points to clusterpublic class DBSCANClusterer<T extends Clusterable<T>>
DBSCAN (density-based spatial clustering of applications with noise) algorithm.
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 Summary | |
|---|---|
DBSCANClusterer(double eps,
int minPts)
Creates a new instance of a DBSCANClusterer. |
|
| Method Summary | |
|---|---|
List<Cluster<T>> |
cluster(Collection<T> points)
Performs DBSCAN cluster analysis. |
double |
getEps()
Returns the maximum radius of the neighborhood to be considered. |
int |
getMinPts()
Returns the minimum number of points needed for a cluster. |
| Methods inherited from class java.lang.Object |
|---|
clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait |
| Constructor Detail |
|---|
public DBSCANClusterer(double eps,
int minPts)
throws NotPositiveException
eps - maximum radius of the neighborhood to be consideredminPts - minimum number of points needed for a cluster
NotPositiveException - if eps < 0.0 or minPts < 0| Method Detail |
|---|
public 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 cluster
NullArgumentException - if the data points are null
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