run_clustering: Perform clustering analysis

In clusterWebApp: Universal Clustering Analysis Platform

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)

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.

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.

Examples

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

clusterWebApp documentation built on Aug. 8, 2025, 6:09 p.m.