Package com.evolveum.midpoint.model.impl.mining.algorithm.cluster.mechanism

Class DensityBasedClustering<T extends Clusterable>

java.lang.Object

com.evolveum.midpoint.model.impl.mining.algorithm.cluster.mechanism.Clusterer<T>

com.evolveum.midpoint.model.impl.mining.algorithm.cluster.mechanism.DensityBasedClustering<T>

public class DensityBasedClustering<T extends Clusterable>
extends Clusterer<T>

Performs density-based clustering of data points based on specified parameters and distance measure. This class implements the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm.

Constructor Summary

Constructors

Constructor	Description
DensityBasedClustering(double eps, int minPts, DistanceMeasure measure, int minRolesOverlap)	Constructs a DensityBasedClustering instance with the specified parameters.

Method Summary

Modifier and Type	Method	Description
List<Cluster<T>>	cluster(Collection<T> points, RoleAnalysisProgressIncrement handler)	Performs density-based clustering on the provided collection of data points.

Methods inherited from class com.evolveum.midpoint.model.impl.mining.algorithm.cluster.mechanism.Clusterer

distance

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Constructor Details

DensityBasedClustering

public DensityBasedClustering(double eps,
 int minPts,
 DistanceMeasure measure,
 int minRolesOverlap)

Constructs a DensityBasedClustering instance with the specified parameters.

Parameters:

eps - The epsilon parameter for distance-based clustering.

minPts - The minimum number of points required to form a dense cluster.

measure - The distance measure for clustering.

minRolesOverlap - The minimum properties overlap required for adding a point to a cluster.

Method Details

cluster

public List<Cluster<T>> cluster(Collection<T> points,
 RoleAnalysisProgressIncrement handler)

Performs density-based clustering on the provided collection of data points.

Specified by:

cluster in class Clusterer<T extends Clusterable>

Parameters:

points - The collection of data points to cluster.

handler - The progress increment handler for tracking the execution progress.

Returns:

A list of clusters containing the clustered data points.