Compute performance indices for clustering solutions.
Usage
cluster_performance(model, ...)
# S3 method for kmeans
cluster_performance(model, ...)
# S3 method for hclust
cluster_performance(model, data, clusters, ...)
# S3 method for dbscan
cluster_performance(model, data, ...)
# S3 method for parameters_clusters
cluster_performance(model, ...)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)
#> # Indices of model performance
#>
#> Sum_Squares_Total | Sum_Squares_Between | Sum_Squares_Within | R2
#> --------------------------------------------------------------------
#> 681.371 | 538.617 | 142.754 | 0.790
# hclust
data <- iris[1:4]
model <- hclust(dist(data))
clusters <- cutree(model, 3)
rez <- cluster_performance(model, data, clusters)
rez
#> # Indices of model performance
#>
#> Sum_Squares_Total | Sum_Squares_Between | Sum_Squares_Within | R2
#> --------------------------------------------------------------------
#> 681.371 | 591.846 | 89.525 | 0.869
# DBSCAN
model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)
rez <- cluster_performance(model, iris[1:4])
rez
#> # Indices of model performance
#>
#> Sum_Squares_Total | Sum_Squares_Between | Sum_Squares_Within | R2
#> --------------------------------------------------------------------
#> 681.371 | 526.424 | 154.947 | 0.773
# Retrieve performance from parameters
params <- model_parameters(kmeans(iris[1:4], 3))
cluster_performance(params)
#> # Indices of model performance
#>
#> Sum_Squares_Total | Sum_Squares_Between | Sum_Squares_Within | R2
#> --------------------------------------------------------------------
#> 681.371 | 602.519 | 78.851 | 0.884