lsdbc (version 0.1.0)

betaCV: BetaCV

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

Value

This function returns the betaCV value.

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.

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

# NOT RUN {
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)
# }