Uses of Class elki.clustering.hierarchical.birch.ClusteringFeature (ELKI: Environment for DeveLoping KDD-Applications Supported by Index-Structures)

Packages that use ClusteringFeature
Package Description

elki.clustering.hierarchical.birch
BIRCH clustering.

Packages that use ClusteringFeature
Package	Description
elki.clustering.hierarchical.birch	BIRCH clustering.

Uses of ClusteringFeature in elki.clustering.hierarchical.birch

Fields in elki.clustering.hierarchical.birch declared as ClusteringFeature
Modifier and Type	Field	Description
`(package private) ClusteringFeature[]`	CFTree.TreeNode.`children`
`private ClusteringFeature`	CFTree.LeafIterator.`current`	Current leaf entry.

Fields in elki.clustering.hierarchical.birch with type parameters of type ClusteringFeature
Modifier and Type	Field	Description
`private java.util.ArrayList<ClusteringFeature>`	CFTree.LeafIterator.`queue`	Queue of open ends.

Methods in elki.clustering.hierarchical.birch that return ClusteringFeature
Modifier and Type	Method	Description
`private ClusteringFeature`	CFTree.`findLeaf(CFTree.TreeNode node, NumberVector nv)`	Find the leaf of a cluster, to get the final cluster assignment.
`ClusteringFeature`	CFTree.`findLeaf(NumberVector nv)`	Find the leaf of a cluster, to get the final cluster assignment.
`ClusteringFeature`	CFTree.LeafIterator.`get()`	Get the current leaf.

Methods in elki.clustering.hierarchical.birch with parameters of type ClusteringFeature
Modifier and Type	Method	Description
`private boolean`	CFTree.`add(ClusteringFeature[] children, ClusteringFeature child)`	Add a node to the first unused slot.
`protected void`	ClusteringFeature.`addToStatistics(ClusteringFeature other)`	Merge an other clustering features.
`private int`	BIRCHLloydKMeans.`assignToNearestCluster(int[] assignment, double[][] means, double[][] cfmeans, ClusteringFeature[] cfs, int[] weights)`	Assign each element to nearest cluster.
`private double[]`	BIRCHLloydKMeans.`calculateVariances(int[] assignment, double[][] means, ClusteringFeature[] cfs, int[] weights)`	Calculate variance of clusters based on clustering features.
`private CFTree.TreeNode`	CFTree.`insert(CFTree.TreeNode node, ClusteringFeature nleaf)`	Recursive insertion.
`private double[][]`	BIRCHLloydKMeans.`kmeans(double[][] cfmeans, ClusteringFeature[] cfs, int[] assignment, int[] weights)`	Perform k-means clustering.
`private double[][]`	BIRCHLloydKMeans.`means(int[] assignment, double[][] means, ClusteringFeature[] cfs, int[] weights)`	Calculate means of clusters.
`protected java.lang.StringBuilder`	CFTree.`printDebug(java.lang.StringBuilder buf, ClusteringFeature n, int d)`	Utility function for debugging.
`private CFTree.TreeNode`	CFTree.`split(CFTree.TreeNode node, ClusteringFeature newchild)`	Split an overfull node.
`double`	BIRCHAbsorptionCriterion.`squaredCriterion(ClusteringFeature f1, ClusteringFeature f2)`	Quality when merging two CFs.
`double`	BIRCHAbsorptionCriterion.`squaredCriterion(ClusteringFeature f1, NumberVector n)`	Quality of a CF when adding a data point
`double`	DiameterCriterion.`squaredCriterion(ClusteringFeature f1, ClusteringFeature f2)`
`double`	DiameterCriterion.`squaredCriterion(ClusteringFeature f1, NumberVector n)`
`double`	EuclideanDistanceCriterion.`squaredCriterion(ClusteringFeature f1, ClusteringFeature f2)`
`double`	EuclideanDistanceCriterion.`squaredCriterion(ClusteringFeature f1, NumberVector n)`
`double`	RadiusCriterion.`squaredCriterion(ClusteringFeature f1, ClusteringFeature f2)`
`double`	RadiusCriterion.`squaredCriterion(ClusteringFeature f1, NumberVector n)`
`double`	AverageInterclusterDistance.`squaredDistance(ClusteringFeature cf1, ClusteringFeature cf2)`
`double`	AverageInterclusterDistance.`squaredDistance(NumberVector v, ClusteringFeature cf)`
`double`	AverageIntraclusterDistance.`squaredDistance(ClusteringFeature cf1, ClusteringFeature cf2)`
`double`	AverageIntraclusterDistance.`squaredDistance(NumberVector v, ClusteringFeature cf)`
`double`	BIRCHDistance.`squaredDistance(ClusteringFeature c1, ClusteringFeature c2)`	Distance between two clustering features.
`double`	BIRCHDistance.`squaredDistance(NumberVector v, ClusteringFeature cf)`	Distance of a vector to a clustering feature.
`double`	CentroidEuclideanDistance.`squaredDistance(ClusteringFeature v, ClusteringFeature cf)`
`double`	CentroidEuclideanDistance.`squaredDistance(NumberVector v, ClusteringFeature cf)`
`double`	CentroidManhattanDistance.`squaredDistance(ClusteringFeature v, ClusteringFeature cf)`
`double`	CentroidManhattanDistance.`squaredDistance(NumberVector v, ClusteringFeature cf)`
`double`	VarianceIncreaseDistance.`squaredDistance(ClusteringFeature cf1, ClusteringFeature cf2)`
`double`	VarianceIncreaseDistance.`squaredDistance(NumberVector v, ClusteringFeature cf)`

Uses of Classelki.clustering.hierarchical.birch.ClusteringFeature

Uses of ClusteringFeature in elki.clustering.hierarchical.birch

Uses of Class
elki.clustering.hierarchical.birch.ClusteringFeature