Title: Universal Clustering Analysis Platform
Version: 0.1.3
Description: An interactive platform for clustering analysis and teaching based on the 'shiny' web application framework. Supports multiple popular clustering algorithms including k-means, hierarchical clustering, DBSCAN (Density-Based Spatial Clustering of Applications with Noise), PAM (Partitioning Around Medoids), GMM (Gaussian Mixture Model), and spectral clustering. Users can upload datasets or use built-in ones, visualize clustering results using dimensionality reduction methods such as Principal Component Analysis (PCA) and t-distributed Stochastic Neighbor Embedding (t-SNE), evaluate clustering quality via silhouette plots, and explore method-specific visualizations and guides. For details on implemented methods, see: Reynolds (2009, ISBN:9781598296975) for GMM; Luxburg (2007) <doi:10.1007/s11222-007-9033-z> for spectral clustering.
License: MIT + file LICENSE
Encoding: UTF-8
Imports: shiny, shinythemes, shinycssloaders, cluster, factoextra, datasets, ggplot2, dbscan, mclust, kernlab, Rtsne, DT, dplyr, tidyr, mlbench, magrittr
RoxygenNote: 7.3.2
NeedsCompilation: no
Packaged: 2025-07-09 11:49:37 UTC; 86136
Author: Yijin Zhou [aut, cre]
Maintainer: Yijin Zhou <yijin_zhou1116@163.com>
Repository: CRAN
Date/Publication: 2025-07-14 16:50:02 UTC

Compute Average Silhouette Width

Description

Calculates the average silhouette coefficient from a silhouette object.

Usage

compute_silhouette(sil)

Arguments

sil

A silhouette object as returned by silhouette.

Value

A numeric value indicating the average silhouette width, or NA if input is NULL.

Examples

data <- scale(iris[, 1:4])
cl <- kmeans(data, 3)$cluster
sil <- cluster::silhouette(cl, dist(data))
if (interactive()) {
  compute_silhouette(sil)
}

Plot Elbow Method for KMeans

Description

Uses within-cluster sum of squares (WSS) to help determine the optimal number of clusters.

Usage

plot_elbow(data)

Arguments

data

A numeric matrix or data frame for clustering.

Value

A ggplot object showing the elbow plot.

Examples

data <- scale(iris[, 1:4])
if (interactive()) {
  plot_elbow(data)
}

Plot Radar Chart for PAM Cluster Centers

Description

Displays the medoids of each PAM cluster using a polar radar chart.

Usage

plot_radar(data, clusters)

Arguments

data

A numeric matrix or data frame for clustering.

clusters

An integer indicating the number of clusters.

Value

A ggplot object showing the radar chart of cluster medoids.

Examples

data <- scale(iris[, 1:4])
if (interactive()) {
  plot_radar(data, clusters = 3)
}

Plot Silhouette Diagram

Description

Plots the silhouette diagram for a given clustering result.

Usage

plot_silhouette(sil)

Arguments

sil

A silhouette object as returned by silhouette.

Value

A silhouette plot if input is not NULL, otherwise a placeholder text.

Examples

data <- scale(iris[, 1:4])
cl <- kmeans(data, 3)$cluster
sil <- cluster::silhouette(cl, dist(data))
if (interactive()) {
  plot_silhouette(sil)
}

Prepare Built-in Datasets for Clustering

Description

Loads and preprocesses a built-in dataset for clustering analysis. Depending on the dataset name provided, different cleaning steps are applied.

Usage

prepare_data(dataset)

Arguments

dataset

A string specifying the dataset name. Options are: "iris", "USArrests", "mtcars", "CO2", "swiss", "Moons".

Details

iris

The classic iris dataset, excluding the species column.

USArrests

State-wise arrest data. Missing values are removed.

mtcars

Motor trend car data set. No transformation applied.

CO2

CO2 uptake in grass plants. Only numeric columns are selected and rows with missing values are removed.

swiss

Swiss fertility and socio-economic indicators. Used as-is.

Moons

Synthetic non-linear dataset generated by mlbench::mlbench.smiley().

Value

A cleaned data.frame containing only numeric variables and no missing values.

Examples

data <- prepare_data("iris")
head(data)

Launch the Shiny Clustering Web App

Description

This function launches the Shiny web application located in the inst/app directory of the installed package. The application provides an interactive interface for clustering analysis.

Usage

run_app()

Value

No return value. This function is called for its side effect (launching the app).

Examples

if (interactive()) {
  run_app()
}

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)
}