# betaCV

##### 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

##### 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*