Package 'hclustTeach'

Title: Hierarchical Cluster Analysis (Learning Didactically)
Description: Implements hierarchical clustering methods (single linkage, complete linkage, average linkage, and centroid linkage) with stepwise printing and dendrograms for didactic purposes.
Authors: Gualberto Segundo Agamez Montalvo [aut, cre]
Maintainer: Gualberto Segundo Agamez Montalvo <[email protected]>
License: MIT + file LICENSE
Version: 0.1.0
Built: 2026-05-13 08:06:19 UTC
Source: https://github.com/cran/hclustTeach

Help Index

Hierarchical Clustering - Average linkage

Description

A function that performs hierarchical clustering with average linkage. It can also print the clustering steps and display a dendrogram

Usage

hclust_average(
  data,
  metric = "euclidean",
  print.steps = TRUE,
  plot = TRUE,
  label.names = TRUE
)

Arguments

data

Numerical matrix or data frame of observations (rows = observations, columns = variables).
metric

Distance metric to be used (default: "euclidean").
print.steps

If TRUE, the algorithm's steps are printed.
plot

If TRUE, a dendrogram is plotted.
label.names

If TRUE, uses the row names as labels in the dendrogram.

Value

object of class "hclust".

Examples

y1 <- c(1, 2, 1, 0); y2 <- c(2, 1, 0, 2)
y3 <- c(8, 8, 9, 7); y4 <- c(6, 9, 8, 9)
Data <- rbind(y1, y2, y3, y4)
hc <- hclust_average(Data, metric = "euclidean",
                     print.steps = TRUE,
                     plot = TRUE,
                     label.names = TRUE)

Hierarchical Clustering - Centroid

Description

A function that performs hierarchical clustering with centroid linkage. It can also print the clustering steps and display a dendrogram

Usage

hclust_centroid(
  data,
  metric = "euclidean",
  print.steps = TRUE,
  plot = TRUE,
  label.names = TRUE
)

Arguments

data

Numerical matrix or data frame of observations (rows = observations, columns = variables).
metric

Distance metric to be used (default: "euclidean").
print.steps

If TRUE, the algorithm's steps are printed.
plot

If TRUE, a dendrogram is plotted.
label.names

If TRUE, uses the row names as labels in the dendrogram.

Value

object of class "hclust".

Examples

y1 <- c(1, 2, 1, 0); y2 <- c(2, 1, 0, 2)
y3 <- c(8, 8, 9, 7); y4 <- c(6, 9, 8, 9)
Data <- rbind(y1, y2, y3, y4)
hc <- hclust_centroid(Data, metric = "euclidean",
                      print.steps = TRUE,
                      plot = TRUE,
                      label.names = TRUE)

Hierarchical Clustering - Complete linkage

Description

A function that performs hierarchical clustering with complete linkage. It can also print the clustering steps and display a dendrogram

Usage

hclust_complete(
  data,
  metric = "euclidean",
  print.steps = TRUE,
  plot = TRUE,
  label.names = TRUE
)

Arguments

data

Numerical matrix or data frame of observations (rows = observations, columns = variables).
metric

Distance metric to be used (default: "euclidean").
print.steps

If TRUE, the algorithm's steps are printed.
plot

If TRUE, a dendrogram is plotted.
label.names

If TRUE, uses the row names as labels in the dendrogram.

Value

object of class "hclust".

Examples

y1 <- c(1, 2, 1, 0); y2 <- c(2, 1, 0, 2)
y3 <- c(8, 8, 9, 7); y4 <- c(6, 9, 8, 9)
Data <- rbind(y1, y2, y3, y4)
hc <- hclust_complete(Data, metric = "euclidean",
                      print.steps = TRUE,
                      plot = TRUE,
                      label.names = TRUE)

Hierarchical Clustering - Single linkage

Description

A function that performs hierarchical clustering with single linkage. It can also print the clustering steps and display a dendrogram

Usage

hclust_single(
  data,
  metric = "euclidean",
  print.steps = TRUE,
  plot = TRUE,
  label.names = TRUE
)

Arguments

data

Numerical matrix or data frame of observations (rows = observations, columns = variables).
metric

Distance metric to be used (default: "euclidean").
print.steps

If TRUE, the algorithm's steps are printed.
plot

If TRUE, a dendrogram is plotted.
label.names

If TRUE, uses the row names as labels in the dendrogram.

Value

object of class "hclust".

Examples

y1 <- c(1, 2, 1, 0); y2 <- c(2, 1, 0, 2)
y3 <- c(8, 8, 9, 7); y4 <- c(6, 9, 8, 9)
Data <- rbind(y1, y2, y3, y4)
hc <- hclust_single(Data, metric = "euclidean",
                    print.steps = TRUE,
                    plot = TRUE,
                    label.names = TRUE)