Document Type

Thesis

Date of Degree Completion

Fall 2017

Degree Name

Master of Science (MS)

Department

Computational Science

Committee Chair

Dr. Boris Kovalerchuk

Second Committee Member

Dr. Razvan Andonie

Third Committee Member

Dr. Szilárd Vajda

Abstract

Abstract Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization by Jacob Brown December 2017 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.

Share

COinS