cluster_performance: Performance of clustering models

In parameters: Processing of Model Parameters

Performance of clustering models

Description

Compute performance indices for clustering solutions.

Usage

cluster_performance(model, ...)

## S3 method for class 'kmeans'
cluster_performance(model, ...)

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

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

## S3 method for class '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

# 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)

parameters documentation built on Nov. 2, 2023, 6:13 p.m.