The separation between clusters is defined by the minimum distances of a point in the cluster to a point in another cluster. The number of clusters are provided. If not, the hierarchical clustering method is used to obtain the clusters. The separation between the clusters for dataset X is calculated. Same is done for dataset PX. An euclidean distance is then calculated between these separation for X and PX.
sep_dist(X, PX, clustering = FALSE, nclust = 3, type = "separation")
X | a data.frame with two or three columns, the first two columns providing the dataset |
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PX | a data.frame with two or three columns, the first two columns providing the dataset |
clustering | LOGICAL; if TRUE, the third column is used as the clustering variable, by default FALSE |
nclust | the number of clusters to be obtained by hierarchical clustering, by default nclust = 3 |
type | character string to specify which measure to use for distance, see ?cluster.stats for details |
distance between X and PX
if(require('fpc')) { with(mtcars, sep_dist(data.frame(wt, mpg, as.numeric(as.factor(mtcars$cyl))), data.frame(sample(wt), mpg, as.numeric(as.factor(mtcars$cyl))), clustering = TRUE)) }#>#> [1] 0.2285099if (require('fpc')) { with(mtcars, sep_dist(data.frame(wt, mpg, as.numeric(as.factor(mtcars$cyl))), data.frame(sample(wt), mpg, as.numeric(as.factor(mtcars$cyl))), nclust = 3)) }#> [1] 0.1069805