plot_elbow

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

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.

Yijin Zhou

clusterWebApp

Universal Clustering Analysis Platform

plot_elbow function

<dl><dt>data</dt>
<dd>A numeric matrix or data frame for clustering.</dd></dl>

Arguments

Plot Elbow Method for KMeans — plot_elbow

<dl>

<dt>data</dt>
<dd>A numeric matrix or data frame for clustering.</dd>

</dl>

plot_elbow: Plot Elbow Method for KMeans

Description

Usage

Value

Arguments

Examples