model_parameters.kmeans: 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 'dbscan'
model_parameters(model, data = NULL, clusters = NULL, ...)

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

## S3 method for class 'pvclust'
model_parameters(model, data = NULL, clusters = NULL, ci = 0.95, ...)

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

## S3 method for class 'hkmeans'
model_parameters(model, ...)

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

## S3 method for class 'pam'
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.
ci	Confidence Interval (CI) level. Default to 0.95 (⁠95%⁠).

Examples

# DBSCAN ---------------------------
if (require("dbscan", quietly = TRUE)) {
  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])
}

#
# 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

#
# pvclust (finds "significant" clusters) ---------------------------
if (require("pvclust", quietly = TRUE)) {
  data <- iris[1:4]
  # NOTE: pvclust works on transposed data
  model <- pvclust::pvclust(datawizard::data_transpose(data, verbose = FALSE),
    method.dist = "euclidean",
    nboot = 50,
    quiet = TRUE
  )

  rez <- model_parameters(model, data, ci = 0.90)
  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-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 K-means (factoextra::hkclust) ----------------------
if (require("factoextra", quietly = TRUE)) {
  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
}

if (require("mclust", quietly = TRUE)) {
  model <- mclust::Mclust(iris[1:4], verbose = FALSE)
  model_parameters(model)
}

#
# K-Medoids (PAM and HPAM) ==============
if (require("cluster", quietly = TRUE)) {
  model <- cluster::pam(iris[1:4], k = 3)
  model_parameters(model)
}
if (require("fpc", quietly = TRUE)) {
  model <- fpc::pamk(iris[1:4], criterion = "ch")
  model_parameters(model)
}

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