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DimVis: Interpreting Visual Clusters in Dimensionality Reduction With Explainable Boosting Machine
DimVis, developed by the ISOVIS Group at Linnaeus University, is an interactive visualization tool for interpreting dimensionality reduction projections of high-dimensional data. It utilizes the supervised Explainable Boosting Machine model for an in-depth understanding of data patterns and cluster formations. Specifically, the tool facilitates users in exploring individual or groups of data points based on single or pairs of features to reveal the underlying structures of the high-dimensional space.
Instructions <br><br> To run DimVis, please follow these steps in your terminal or command prompt:
- Launch Anaconda Navigator and open a PowerShell prompt.
- Activate the environment by running: conda activate your_environment
- Navigate to the application directory: cd the_directory
- Start the application: python main.py
Ensure you have the necessary dependencies listed in the requirements installed in your_environment to avoid any issues.
Author <br> Parisa Salmanian <br><br> Advisors <br> Dr. Rafael Messias Martins, Dr. Angelos Chatzimparmpas, and Dr. Ali Can Karaca
Department of Computer Science and Media Technology, Faculty of Technology, Linnaeus University, Sweden