Package elki.clustering.hierarchical.birch

BIRCH clustering.

Interface Summary

Interface	Description
BIRCHAbsorptionCriterion	BIRCH absorption criterion.
BIRCHDistance	Distance function for BIRCH clustering.

Class Summary

Class	Description
AverageInterclusterDistance	Average intercluster distance.
AverageInterclusterDistance.Par	Parameterization class.
AverageIntraclusterDistance	Average intracluster distance.
AverageIntraclusterDistance.Par	Parameterization class.
BIRCHKMeansPlusPlus	K-Means++-like initialization for BIRCH k-means; this cannot be used to initialize regular k-means, use KMeansPlusPlus instead.
BIRCHKMeansPlusPlus.Par	Parameterization class.
BIRCHLeafClustering	BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
BIRCHLeafClustering.Par	Parameterization class.
BIRCHLloydKMeans	BIRCH-based clustering algorithm that simply treats the leafs of the CFTree as clusters.
BIRCHLloydKMeans.Par	Parameterization class.
CentroidEuclideanDistance	Centroid Euclidean distance.
CentroidEuclideanDistance.Par	Parameterization class.
CentroidManhattanDistance	Centroid Manhattan Distance
CentroidManhattanDistance.Par	Parameterization class.
CFTree	Partial implementation of the CFTree as used by BIRCH.
CFTree.Factory	CF-Tree Factory.
CFTree.Factory.Par	Parameterization class for CFTrees.
CFTree.LeafIterator	Iterator over leaf nodes.
ClusteringFeature	Clustering Feature of BIRCH
DiameterCriterion	Average Radius (R) criterion.
DiameterCriterion.Par	Parameterization class
EuclideanDistanceCriterion	Distance criterion.
RadiusCriterion	Average Radius (R) criterion.
RadiusCriterion.Par	Parameterization class
VarianceIncreaseDistance	Variance increase distance.
VarianceIncreaseDistance.Par	Parameterization class.