four_clusters

This dataset (<code>four_clusters</code>) contains simulated data with four distinct clusters,
each generated using different shapes and scales. It is ideal for demonstrating
clustering algorithms and visualization techniques.

datasets

A 'shiny' application for visualizing high-dimensional data
using non-linear dimensionality reduction (NLDR) techniques such as
t-SNE and UMAP. It provides an interactive platform to explore
high-dimensional datasets, diagnose the quality of the embeddings
using the 'quollr' package, and compare different NLDR methods.

Divendra Yadav

polarisR

Non-Linear Dimensionality Reduction Visualization Tool

Ursula Laa

Jayani P. Gamage

Eun-Kyung Lee

four_clusters function

A data frame with 2000 rows and 4 variables:<dl>
<dt>x1</dt>
<dd>Numeric. First feature coordinate.</dd><dt>x2</dt>
<dd>Numeric. Second feature coordinate.</dd><dt>x3</dt>
<dd>Numeric. Third feature coordinate.</dd><dt>x4</dt>
<dd>Numeric. Fourth feature coordinate.</dd><dt>cluster</dt>
<dd>Factor. The cluster label (1, 2, 3, or 4).</dd>
</dl>

Format

Four Clusters Simulated Dataset — four_clusters

A data frame with 2000 rows and 4 variables:<dl>
<dt>x1</dt>
<dd>Numeric. First feature coordinate.</dd>

<dt>x2</dt>
<dd>Numeric. Second feature coordinate.</dd>

<dt>x3</dt>
<dd>Numeric. Third feature coordinate.</dd>

<dt>x4</dt>
<dd>Numeric. Fourth feature coordinate.</dd>

<dt>cluster</dt>
<dd>Factor. The cluster label (1, 2, 3, or 4).</dd>


</dl>

four_clusters: Four Clusters Simulated Dataset

Description

Usage

Arguments

Format

Details

References

Examples