Welcome to the Visual Knowledge Discovery and Imaging Lab, directed by Dr. Boris Kovalerchuk. We are located at the Department of Computer Science in the College of the Sciences of Central Washington University.
Our lab focuses on Visual Knowledge Discovery (VKD) and Machine Learning (ML) from inherently interpretable visual paradigms. We use methodologies and insights that have been detailed in foundational works such as Dr. Kovalerchuk's books including significantly "Visual Knowledge Discovery and Machine Learning" (2018) and student assisted conference publications. Works contained use "generalized Parallel and Radial coordinates with a new concept of General Line Coordinates (GLC)" (Kovalerchuk 2018).
This GitHub organization contains research projects conducted by students under the guidance of Dr. Boris Kovalerchuk, contributing to the fields of high-dimensional data visualization and machine learning techniques for identifying complex multidimensional data patterns and constructing interpretable machine learning models. For more information please see Dr. Kovalerchuk's publications.
| 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 |
(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.
