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:
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]