Performance of clustering models — cluster

Compute performance indices for clustering solutions.

Usage

cluster_performance(model, ...)

# S3 method for class 'hclust'
cluster_performance(model, data, clusters, ...)

Arguments

model: Cluster model.
...: Arguments passed to or from other methods.
data: A data frame.
clusters: A vector with clusters assignments (must be same length as rows in data).

Examples

# kmeans
model <- kmeans(iris[1:4], 3)
cluster_performance(model)
#>   Sum_Squares_Total Sum_Squares_Between Sum_Squares_Within        R2
#> 1          681.3706            602.5192           78.85144 0.8842753

# hclust
data <- iris[1:4]
model <- hclust(dist(data))
clusters <- cutree(model, 3)
cluster_performance(model, data, clusters)
#>   Sum_Squares_Total Sum_Squares_Between Sum_Squares_Within        R2
#> 1          681.3706            591.8456           89.52501 0.8686104

# Retrieve performance from parameters
params <- model_parameters(kmeans(iris[1:4], 3))
cluster_performance(params)
#>   Sum_Squares_Total Sum_Squares_Between Sum_Squares_Within        R2
#> 1          681.3706            538.6171           142.7535 0.7904906