Class %DeepSee.extensions.clusters.CalinskiHarabasz

class %DeepSee.extensions.clusters.CalinskiHarabasz extends %RegisteredObject

This class calculates Calinski-Harabasz index. Calinski-Harabasz use the Variance Ratio Criterion which is analogous to F-Statistics to estimate the number of clusters a given data naturally falls into. They minimize Within Cluster/Group Sum of Squares and maximize Between Cluster/Group Sum of Squares.

Validity indices are used in Cluster Validation and determination of the optimal number of clusters.

Inventory

Parameters	Properties	Methods	Queries	Indices	ForeignKeys	Triggers
	3	7

Summary

Properties
Model	SubsetKey	normalize

Methods
%%OIDGet	%AddToSaveSet	%ClassIsLatestVersion	%ClassName
%ConstructClone	%DispatchClassMethod	%DispatchGetModified	%DispatchGetProperty
%DispatchMethod	%DispatchSetModified	%DispatchSetMultidimProperty	%DispatchSetProperty
%Extends	%GetParameter	%IsA	%IsModified
%New	%NormalizeObject	%ObjectModified	%OriginalNamespace
%PackageName	%RemoveFromSaveSet	%SerializeObject	%SetModified
%ValidateObject	GetSubsetCentroids	calculate	calculateForSample
traceB	traceBSubset	traceW	traceWSubset

Properties

• property Model as AbstractModel;

• property SubsetKey as %Integer;

• property normalize as %Boolean [ InitialExpression = 1 ];

Methods

• method GetSubsetCentroids(Output zz)

• method calculate(Output sc As %Status) as %Double

• method calculateForSample(SampleSize As %Integer, Output sc As %Status) as %Double

• method traceB() as %Double

• method traceBSubset(zz) as %Double

• method traceW() as %Double

• method traceWSubset(zz) as %Double


[BASEXML] > [%DeepSee] > [extensions] > [clusters] > [CalinskiHarabasz]		Private Storage