clusterSOM {somhca}R Documentation

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)

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'.

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.

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()

[Package somhca version 0.2.0 Index]