parameters (version 0.22.0)

cluster_performance: Performance of clustering models

Description

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)
# hclust
data <- iris[1:4]
model <- hclust(dist(data))
clusters <- cutree(model, 3)

rez <- cluster_performance(model, data, clusters)
rez
if (FALSE) { # require("dbscan", quietly = TRUE)
# DBSCAN
model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)

rez <- cluster_performance(model, iris[1:4])
rez
}
# Retrieve performance from parameters
params <- model_parameters(kmeans(iris[1:4], 3))
cluster_performance(params)