AI/ML integration with visualization is presented in the form of emerging Visual Knowledge Discovery (VKD) ecosystem that is a set of interconnected algorithms, software projects, and platforms that co-evolve around a VKD shared technological base. Interpretable Machine Learning (ML) algorithms and lossless visualization of high-dimensional data are the core of VKD for the end users to discover trustworthy models.
The search on this site allows you to find speccific AI/VKD sources (papers, presentations, software) from Visual Knowledge Discovery Lab at Central WAshington University. Several publications are linked to respective software at the GitHub site of CWU-VKD-LAB allowing experiments with software.
| Visual Knowledge Discovery and General Line Coordinates Concepts | Abbreviation |
|---|---|
| Visual Knowledge discovery | VKD |
| General Line Coordinates | GLC |
| Full 2-D Machine Learning Paradigm | Full 2D ML |
| Computational and Interactive Visual Learning | CIVL |
| Parallel Coordinates | PC |
| Radial Coordinates | RC |
| General Line Coordinates - Linear | GLC-L |
| Collocated Paired Coordinates | CPC |
| Shifted Paired Coordinates | SPC |
| Shifted Paired Coordinates for Decision Tree | SPC-DT |
| Bended Coordinates for Decision Tree | BC-DT |
| Stick Figures | SF |
| Combined SPC and Stick Figures for Glyphs | SPC-SF |
| Concentric Coordinates | CoC |
| Concentric Coordinates for k-Nearest Neighbors | CoC-kNN |
| Elliptic Paired Coordinates | EPC |
| Dynamic Circular Coordinates | DCC |
| Static Circular Coordinates | SCC |
| Dynamic Scaffolding Coordinates 1 | DSC1 |
| Dynamic Scaffolding Coordinates 2 | DSC2 |
| In Line Coordinates | ILC |
| Sequential Rule Generation Algorithms | SRG |
| Monotone Boolean Function | MBF |
| MBF Multiple Disc Form visualization | MBF MDF |
| Monotone Original Expert Knowledge Acquisition | MOEKA |
| GLC Divide & Classify Process | D&C |
| Combined SPC with several CPC | SCPC |