model_parameters.hclust: Parameters from Cluster Models (k-means, ...)

In parameters: Processing of Model Parameters

Parameters from Cluster Models (k-means, ...)

Description

Format cluster models obtained for example by kmeans().

Usage

## S3 method for class 'hclust'
model_parameters(model, data = NULL, clusters = NULL, ...)

Arguments

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

Examples

#
# K-means -------------------------------
model <- kmeans(iris[1:4], centers = 3)
rez <- model_parameters(model)
rez

# Get clusters
predict(rez)

# Clusters centers in long form
attributes(rez)$means

# Between and Total Sum of Squares
attributes(rez)$Sum_Squares_Total
attributes(rez)$Sum_Squares_Between

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

rez <- model_parameters(model, data, clusters)
rez

# Get clusters
predict(rez)

# Clusters centers in long form
attributes(rez)$means

# Between and Total Sum of Squares
attributes(rez)$Total_Sum_Squares
attributes(rez)$Between_Sum_Squares

#
# Hierarchical K-means (factoextra::hkclust) ----------------------
data <- iris[1:4]
model <- factoextra::hkmeans(data, k = 3)

rez <- model_parameters(model)
rez

# Get clusters
predict(rez)

# Clusters centers in long form
attributes(rez)$means

# Between and Total Sum of Squares
attributes(rez)$Sum_Squares_Total
attributes(rez)$Sum_Squares_Between

# K-Medoids (PAM and HPAM) ==============
model <- cluster::pam(iris[1:4], k = 3)
model_parameters(model)

model <- fpc::pamk(iris[1:4], criterion = "ch")
model_parameters(model)

# DBSCAN ---------------------------
model <- dbscan::dbscan(iris[1:4], eps = 1.45, minPts = 10)

rez <- model_parameters(model, iris[1:4])
rez

# Get clusters
predict(rez)

# Clusters centers in long form
attributes(rez)$means

# Between and Total Sum of Squares
attributes(rez)$Sum_Squares_Total
attributes(rez)$Sum_Squares_Between

# HDBSCAN
model <- dbscan::hdbscan(iris[1:4], minPts = 10)
model_parameters(model, iris[1:4])

parameters documentation built on April 4, 2025, 3:16 a.m.