Welcome to the Visual Knowledge Discovery and Imaging Lab (VKD Lab), directed by Dr. Boris Kovalerchuk. We are part of the Department of Computer Science in the College of the Sciences at Central Washington University.
Our research explores Visual Knowledge Discovery (VKD) and interpretable Machine Learning (ML) through visual paradigms. We build on foundational works such as Dr. Kovalerchuk’s Visual Knowledge Discovery and Machine Learning (2018), emphasizing General Line Coordinates (GLC) including Parallel, Radial, and newer task-specific coordinate systems for multidimensional, lossless data visualization, and visual computation.
This GitHub organization hosts projects developed by students under Dr. Kovalerchuk’s mentorship, focused on high-dimensional data visualization and the development of interpretable machine learning methods. For related publications, see Dr. Kovalerchuk’s site.
| Program Name | Task Addressed | General Line Coordinate Type | Repository Link |
|---|---|---|---|
| VisCanvas 2.0 | Visualization software for n-D data plotting and analysis | Parallel Coordinates. | Link |
| DV 2.0 | This application displays n-Dimensional data in 2D using GLC-Linear (GLC-L) coordinates. | GLC-L | Link |
| Moeka | When there is no training data and we perform expert interview to build model. | Hansel Chain Visualization | Link |
| DCVis | Build visual machine learning models with multidimensional general line coordinate visualizations by interactive classification and synthetic data generation tools. | Parallel Coordinates, Adjusted Parallel Coordinates, Shifted Paired Coordinates, Dynamic Scaffold Coordinates 1 & 2, Static Circle Coordinates, Dynamic Circle Coordinates | Link |
| DSCVis | Dynamic Scaffold Coordinates Visualization System build for worst split analysis. | Parallel Coordinates, Shifted Paired Coordinates, Dynamic Scaffold Coordinates 1 & 2 | Link |
| JTabViz | Multiplatform interactive computational data analysis tool to build visual machine learning models from General Line Coordinate multidimensional lossless visualizations. | Parallel Coordinates, Shifted Paired Coordinates, Collocated Paired Coordinates, Static/Dynamic Circle/poylgon Coordinates, Freeform Circle Coordinates, Radial/Star Coordinates, Concentric Coordinates, In-Line Coordinates | Link |
| ML Classifier Comparison Tool | ML benchmarking tool to load data, select from 21 standard ML classifiers, and set hyperparameters. Results tabulated and visualized in Parallel Coordinates for F1, Recall, and Accuracy. | Parallel Coordinates | Link |
| SPC-DT | Visualizes decision trees using paired GLCs. | SPC Decision Tree | Link |
| SPC-SF | Hybrid bird glyph visualization. | SPC Glyph | Link |
| SPC-3D | A 3D shifted pair coordinates program. | SPC 3D | Link |
| DV | Previous DV version. | GLC-L | Link |
| HB CUDA | High-performance generation of Hyperblocks (HBs) using CUDA GPU accelerations. | N/A (Computing Only) | Link |
(This chart is a work in progress, to our lab contributors, please feel free to update with your own work.)
If you encounter any issues in using our projects, please submit an issue report on the relevant project's GitHub page, please include:
- Environment details (OS, software version, language used version)
- A detailed description of the issue
- Resultant behavior and expected behavior
- Steps to reproduce the issue, if possible
All projects contained under this organization are freely available under the MIT License, allowing for both personal and commercial use. For the full license text, please refer to the LICENSE file in each project repository.
