| Type: | Package |
| Title: | Hierarchical Cluster Analysis (Learning Didactically) |
| Version: | 0.1.0 |
| Description: | Implements hierarchical clustering methods (single linkage, complete linkage, average linkage, and centroid linkage) with stepwise printing and dendrograms for didactic purposes. |
| License: | MIT + file LICENSE |
| Encoding: | UTF-8 |
| RoxygenNote: | 7.3.2 |
| NeedsCompilation: | no |
| Packaged: | 2025-09-18 14:57:50 UTC; gsaga |
| Author: | Gualberto Segundo Agamez Montalvo [aut, cre] |
| Maintainer: | Gualberto Segundo Agamez Montalvo <gsagamez@dema.ufc.br> |
| Repository: | CRAN |
| Date/Publication: | 2025-09-23 10:30:02 UTC |
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)