clusterWebApp (version 0.1.3)

run_clustering: Perform clustering analysis

Description

This function performs clustering on a numeric matrix using one of six common clustering methods: KMeans, Hierarchical, DBSCAN, PAM, Gaussian Mixture Model (GMM), or Spectral Clustering.

Usage

run_clustering(data, method, k = 3, eps = 0.5, minPts = 5)

Value

A list containing two elements:

cluster

A vector of cluster labels assigned to each observation.

silhouette

An object of class silhouette representing silhouette widths.

Arguments

data

A numeric matrix or data frame, typically standardized, to be clustered.

method

A string indicating the clustering method to use. Options are: "KMeans", "Hierarchical", "DBSCAN", "PAM", "GMM", "Spectral".

k

An integer specifying the number of clusters. Required for KMeans, Hierarchical, PAM, GMM, and Spectral.

eps

A numeric value specifying the epsilon parameter for DBSCAN. Default is 0.5.

minPts

An integer specifying the minimum number of points for DBSCAN. Default is 5.

Examples

data(iris)
result <- run_clustering(scale(iris[, 1:4]), method = "KMeans", k = 3)
print(result$cluster)
if (interactive()) {
  plot(result$silhouette)
}