betaCV

0th

Percentile

BetaCV

function to calculates the BetaCV.

Keywords
betaCV, cluster validation
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.

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

Aliases
  • betaCV
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)
# }
Documentation reproduced from package lsdbc, version 0.1.0, License: GPL

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