IndexPartitionDunnIndex Method

public static double DunnIndex(
	DoubleMatrix data,
	IndexPartition<double> partition
)

Public Shared Function DunnIndex ( 
	data As DoubleMatrix,
	partition As IndexPartition(Of Double)
) As Double

public:
static double DunnIndex(
	DoubleMatrix^ data, 
	IndexPartition<double>^ partition
)

static member DunnIndex : 
        data : DoubleMatrix * 
        partition : IndexPartition<float> -> float 

Parameters

data  DoubleMatrix

The data whose rows represent the available observations.

partition  IndexPartitionDouble

The data partition to evaluate.

Return Value

Each column of data is associated to one of the variables under study, while its rows are associated to the individuals. The partition is intended to define parts which contains row indexes valid for data.

The Dunn index aims to identify dense and well-separated clusters. It is defined as the ratio between the minimal inter-cluster distance to maximal intra-cluster distance. Since this criterion seeks clusters with high intra-cluster similarity and low inter-cluster similarity, clusters with high Dunn index are more desirable.

This method applies Euclidean distances. The intra-cluster distance is implemented as the maximal distance between any pair of elements in the cluster. The inter-cluster distance is implemented as the single linkage, i.e. the shortest distance between pairs of individuals belonging to different clusters.

Reference

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