Package org.hipparchus.clustering
Class DBSCANClusterer<T extends Clusterable>
java.lang.Object
org.hipparchus.clustering.Clusterer<T>
org.hipparchus.clustering.DBSCANClusterer<T>
- Type Parameters:
T
- type of the points to cluster
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:
- eps: the distance that defines the ε-neighborhood of a point
- minPoints: the minimum number of density-connected points required to form a cluster
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Constructor Summary
ConstructorDescriptionDBSCANClusterer
(double eps, int minPts) Creates a new instance of a DBSCANClusterer.DBSCANClusterer
(double eps, int minPts, DistanceMeasure measure) Creates a new instance of a DBSCANClusterer. -
Method Summary
Methods inherited from class org.hipparchus.clustering.Clusterer
distance, getDistanceMeasure
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Constructor Details
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DBSCANClusterer
Creates a new instance of a DBSCANClusterer.The euclidean distance will be used as default distance measure.
- Parameters:
eps
- maximum radius of the neighborhood to be consideredminPts
- minimum number of points needed for a cluster- Throws:
MathIllegalArgumentException
- ifeps < 0.0
orminPts < 0
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DBSCANClusterer
public DBSCANClusterer(double eps, int minPts, DistanceMeasure measure) throws MathIllegalArgumentException Creates a new instance of a DBSCANClusterer.- Parameters:
eps
- maximum radius of the neighborhood to be consideredminPts
- minimum number of points needed for a clustermeasure
- the distance measure to use- Throws:
MathIllegalArgumentException
- ifeps < 0.0
orminPts < 0
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Method Details
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getEps
public double getEps()Returns the maximum radius of the neighborhood to be considered.- Returns:
- maximum radius of the neighborhood
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getMinPts
public int getMinPts()Returns the minimum number of points needed for a cluster.- Returns:
- minimum number of points needed for a cluster
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cluster
Performs DBSCAN cluster analysis.- Specified by:
cluster
in classClusterer<T extends Clusterable>
- Parameters:
points
- the points to cluster- Returns:
- the list of clusters
- Throws:
NullArgumentException
- if the data points are null
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