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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.

Property Inventory (Including Private)

Method Inventory (Including Private)

Properties (Including Private)

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 (Including Private)

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 Members

Inherited Methods (Including Private)

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