{"id":{"repo_id":"central-wash","oai_identifier":"oai:digitalcommons.cwu.edu:etd-1861"},"canonical_url":"https://search.dev.ndltd.org/etd/central-wash/oai:digitalcommons.cwu.edu:etd-1861","repository":{"repo_id":"central-wash","name":"Central Washington University","base_url":"https://digitalcommons.cwu.edu/do/oai/"},"display":{"title":"Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization","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.","abstract_html":"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.","abstract_has_math":false,"creators":["Brown, Jacob"],"institution":null,"degree_name":"Master of Science (MS)","degree_level":null,"degree_discipline":"Computational Science","degree_department":null,"school":null,"contributors":["Boris Kovalerchuk","Razvan Andonie","Szilárd Vajda"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2017,"date_issued":"2017-01-01T08:00:00Z","date_published":"2017-01-01T08:00:00Z","updated_at":"2026-07-24T01:37:15Z","subjects":["Pareto Optimization","Pareto Frontier","Linear General Line Coordinates","Parallel Coordinates","Interactive Decision Maker","Euclidean Weights","Graphics and Human Computer Interfaces"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://digitalcommons.cwu.edu/etd/898","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Boris Kovalerchuk","Razvan Andonie","Szilárd Vajda"]},{"key":"dc:creator","label":"Author","values":["Brown, Jacob"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.available","label":"Dc Date Available","values":["2018-02-22T08:00:00Z"]},{"key":"dc:type","label":"Dc Type","values":["Text"]},{"key":"thesis:degree_discipline","label":"Discipline","values":["Computational Science"]},{"key":"thesis:degree_name","label":"Degree Name","values":["Master of Science (MS)"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Pareto Optimization","Pareto Frontier","Linear General Line Coordinates","Parallel Coordinates","Interactive Decision Maker","Euclidean Weights","Graphics and Human Computer Interfaces"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://digitalcommons.cwu.edu/etd/898"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["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."]},{"key":"dc:title","label":"Title","values":["Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization"]}]}],"canonical_facts":{"dc:contributor":["Boris Kovalerchuk","Razvan Andonie","Szilárd Vajda"],"dc:creator":["Brown, Jacob"],"dc:date.available":["2018-02-22T08:00:00Z"],"dc:description.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."],"dc:identifier":["https://digitalcommons.cwu.edu/etd/898"],"dc:language":["English"],"dc:subject":["Pareto Optimization","Pareto Frontier","Linear General Line Coordinates","Parallel Coordinates","Interactive Decision Maker","Euclidean Weights","Graphics and Human Computer Interfaces"],"dc:title":["Visualizing Multidimensional Data with General Line Coordinates and Pareto Optimization"],"dc:type":["Text"],"thesis:degree_discipline":["Computational Science"],"thesis:degree_name":["Master of Science (MS)"]},"updated_at":"2026-07-24T01:37:15Z"}