somhca (version 0.2.0)

clusterSOM: Perform Clustering on SOM Nodes

Description

Groups similar nodes of the SOM using hierarchical clustering and the KGS penalty function to determine the optimal number of clusters.

Usage

clusterSOM(model, plot_result = TRUE, input = NULL)

Value

A plot of the clusters on the SOM grid (if `plot_result = TRUE`). If `input` is provided, the clustered dataset is stored in a package environment for retrieval.

Arguments

model

A trained SOM model object.

plot_result

A logical value indicating whether to plot the clustering result. Default is `TRUE`.

input

An optional input specifying either:

File Path

A string specifying the path to a CSV file.

In-Memory Data

A data frame or matrix containing numeric data.

If provided, clusters are assigned to the observations in the original dataset, and the updated data is stored in a package environment as 'DataAndClusters'.

Examples

# Create a toy matrix with 9 columns and 100 rows
data <- matrix(rnorm(900), ncol = 9, nrow = 100)  # 900 random numbers, 100 rows, 9 columns

# Run the finalSOM function with the mock data
model <- finalSOM(data, dimension = 6, iterations = 700)

# Example 1: Perform clustering using the mock model
clusterSOM(model, plot_result = TRUE)

# Example 2: Cluster with an in-memory toy data frame
df <- data.frame(
  ID = paste0("Sample", 1:100), # Character column for row headings
  matrix(rnorm(900), ncol = 9, nrow = 100) # Numeric data
)
clusterSOM(model, plot_result = FALSE, input = df)
getClusterData()

# Example 3: Load toy data from a CSV file, perform clustering, and retrieve the clustered dataset
file_path <- system.file("extdata", "toy_data.csv", package = "somhca")
clusterSOM(model, plot_result = FALSE, input = file_path)
getClusterData()