Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization

These results will show that the use of Linear General Line Coordinates (GLC-L) can visualize multidimensional data better than typical methods, such as Parallel Coordinates (PC). The results of using GLC-L will display visuals with less clutter than PC and be easier to see changes from one graph to the next. Visualizing the Pareto Frontier with GLC-L allows n-D data to be viewed at once, compared to typical methods that are limited to 2 or 3 objectives at a time. This method details the process of selecting a ”best” case, from a group of equals in the Pareto Subset and comparing it against an optimal solution. Selecting a ”best” case from a Pareto Subset is difficult, because every individual is better in some ways to its peers. The ”best” case is the solution to the specific task for each dataset.

Name thesis:degree_name

Master of Science (MS)

Discipline thesis:degree_discipline

Computational Science

Year dc:date.available

2017

Author dc:creator

• Brown, Jacob

Contributors dc:contributor

• Boris Kovalerchuk
• Razvan Andonie
• Szilárd Vajda

• Pareto Optimization
• Pareto Frontier
• Linear General Line Coordinates
• Parallel Coordinates
• Interactive Decision Maker
• Euclidean Weights
• Graphics and Human Computer Interfaces

Language dc:language

English

Repository record dc:identifier

https://digitalcommons.cwu.edu/etd/898

OAI identifier oai:identifier

oai:digitalcommons.cwu.edu:etd-1861