IndexPartitionDaviesBouldinIndex Method

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

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

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

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

Parameters

data

Type: Novacta.AnalyticsDoubleMatrix
The data whose rows represent the available observations.

partition

Type: Novacta.AnalyticsIndexPartitionDouble
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 Davies-Bouldin index takes on small values for partitions with high similarity among observations in each part and low similarities among parts. As a consequence, the best partition is considered the one with the smallest Davies–Bouldin index.

This method applies Euclidean distances. The intra-cluster distance is implemented as the centroid diameter, or the average distance between the elements in the cluster and the cluster centroid. The inter-cluster distance is implemented as the centroid linkage, i.e. the distance between cluster centroids.

Reference

Other Resources