betaCV: BetaCV

In lsdbc: Locally Scaled Density Based Clustering

Description

function to calculates the BetaCV.

Usage

betaCV(clust,dist)

Arguments

clust	Determine in which cluster a data is belonged. clust should be a numeric, 0 indicates a noise and cluster start at 1.
dist	Distance matrix

Details

BetaCV measures how well the clusters based on compactness (intra-cluster distance) and separability (inter-cluster distance). BetaCV is the ratio between the average of intra-cluster distance to the average of inter-claster distance. The smaller BetaCV value indicates the better the clustering.

Value

This function returns the betaCV value.

Author(s)

Fella Ulandari and Robert Kurniawan

References

University of Illinois. (2020, January 10). 6.1 Methods for Clustering Validation. Retrieved from Coursera: https://www.coursera.org/lecture/cluster-analysis/6-1-methods-for-clustering-validation-k59pn

See Also

https://www.coursera.org/lecture/cluster-analysis/6-1-methods-for-clustering-validation-k59pn

Examples

x <- runif(20,-1,1)
y <- runif(20,-1,1)
dataset <- cbind(x,y)
l <- lsdbc(dataset, 7,3,"euclidean")

dmat <- as.matrix(dist(dataset,"euclidean"))
betaCV(l$cluster,dmat)

Example output

[1] 0.2681999

lsdbc documentation built on July 1, 2020, 6:01 p.m.