RadViz-Plotly

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Overview

RadViz-Plotly is an open-source Python package designed for data scientists to create 2D and 3D Radial Visualization (RadViz) plots for high-dimensional datasets. These visualizations provide a comprehensive view of data distribution across dimensions, enabling better understanding and discovery of hidden insights. The package leverages the powerful interactive tools of the Plotly library.

Key Features

• 2DRadViz: Generates 2D Radial Visualizations.
• 3DRadViz: Creates 3D Radial Visualizations.
• Interactive Visualizations: Zoom, filter labels, and rotate 3D graphs.

Quickstart

Without Local Installation

You can try RadViz-Plotly without installing it locally by following these steps:

1. Click here to access interactive Jupyter notebooks. This repository contains three folders, each with datasets and a Jupyter notebook demonstrating RadViz-Plotly.
   • Note: The Jupyter environment may take some time to load.
2. Open one of the folders (e.g., Car Evaluation Dataset, Election Dataset, or Iris Dataset).
3. Open the file with the .ipynb extension (Jupyter notebook).
4. From the Cell menu in the toolbar, select Run All to execute the notebook.

Hints:

• The first run may take time to install dependencies.
• The package supports interactive features like zooming, filtering labels, and rotating 3D graphs.
• RadViz-Plotly depends on the Plotly library.

With Local Installation

1. Install RadViz-Plotly using pip:

   pip install RadViz-Plotly

2. Clone the examples repository:

   git clone https://github.com/elewah/RadViz-Plotly-Examples.git

   Alternatively, download the repository as a ZIP file here.
3. Run the examples using Jupyter Lab to understand how to use the RadViz-Plotly package.

Gallery

About

RadViz-Plotly was developed by a research group at the IoT Lab, Ontario Tech University. It provides tools for creating 2D and 3D Radial Visualizations, enabling data scientists to explore high-dimensional datasets interactively.

Citation

If you use RadViz-Plotly in your research, please cite the following paper:

A. Elewah, A. A. Badawi, H. Khalil, S. Rahnamayan, and K. Elgazzar, "3D-RadViz: Three Dimensional Radial Visualization for Large-Scale Data Visualization," 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, pp. 1037-1046, doi: 10.1109/CEC45853.2021.9504983.

License

This project is licensed under the MIT License.