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%DeepSee.extensions.clusters.CalinskiHarabasz

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

Properties

property Model as AbstractModel;
Property methods: ModelGet(), ModelGetSwizzled(), ModelIsValid(), ModelNewObject(), ModelSet()
property SubsetKey as %Integer;
Property methods: SubsetKeyDisplayToLogical(), SubsetKeyGet(), SubsetKeyIsValid(), SubsetKeyLogicalToDisplay(), SubsetKeyNormalize(), SubsetKeySet()
property normalize as %Boolean [ InitialExpression = 1 ];
Property methods: normalizeDisplayToLogical(), normalizeGet(), normalizeIsValid(), normalizeLogicalToDisplay(), normalizeNormalize(), normalizeSet()

Methods

method GetSubsetCentroids(Output zz) [ Language = objectscript ]
method calculate(Output sc As %Status) as %Double [ Language = objectscript ]
method calculateForSample(SampleSize As %Integer, Output sc As %Status) as %Double [ Language = objectscript ]
method traceB() as %Double [ Language = objectscript ]
method traceBSubset(zz) as %Double [ Language = objectscript ]
method traceW() as %Double [ Language = objectscript ]
method traceWSubset(zz) as %Double [ Language = objectscript ]

Inherited Methods

%AddToSaveSet() %DispatchSetModified() %NormalizeObject()
%ClassIsLatestVersion() %DispatchSetMultidimProperty() %ObjectModified()
%ClassName() %DispatchSetProperty() %OriginalNamespace()
%ConstructClone() %Extends() %PackageName()
%DispatchClassMethod() %GetParameter() %RemoveFromSaveSet()
%DispatchGetModified() %IsA() %SerializeObject()
%DispatchGetProperty() %IsModified() %SetModified()
%DispatchMethod() %New() %ValidateObject()