Document Type
Thesis
Date of Degree Completion
Fall 2017
Degree Name
Master of Science (MS)
Department
Computational Science
Committee Chair
Boris Kovalerchuk
Second Committee Member
Razvan Andonie
Third Committee Member
Szilárd Vajda
Abstract
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.
Recommended Citation
Brown, Jacob, "Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization" (2017). All Master's Theses. 898.
https://digitalcommons.cwu.edu/etd/898
Language
English