Enhancing parallel coordinates and RadVis visualizations using single-and multi-objective optimization

dc.contributor.advisorRahnamayan, Shahryar
dc.contributor.authorAldwib, Khiria
dc.date.accessioned2021-05-25T18:16:22Z
dc.date.accessioned2022-03-29T16:46:15Z
dc.date.available2021-05-25T18:16:22Z
dc.date.available2022-03-29T16:46:15Z
dc.date.issued2021-04-01
dc.degree.disciplineElectrical and Computer Engineering
dc.degree.levelMaster of Applied Science (MASc)
dc.description.abstractData visualization is crucial to discover hidden patterns and relationships in high dimensional datasets; visualization is an essential branch in data analytics applied in science and engineering fields. This thesis has targeted two state-of-the-art methods from two powerful families of visualization techniques: one with dimension reduction, Radial Coordinate Visualization (RadViz), and the other without dimension reduction, for instance, Parallel Coordinates Plot (PCP). In improving these techniques, evolutionary algorithms have been utilized to determine the optimal ordering of coordinates by considering single- and multi-objectives; using this concept, a smart mutation operator has been proposed and tested comprehensively. In order to investigate the performance of visualization proposed schemes, a benchmark dataset has been proposed, and objective and subjective assessments have been conducted. This investigation shows that the optimal ordering of coordinates can influence crucially visualization results. This thesis’s findings can be utilized to enhance other largescale visualization techniques used in visual-data analytics areas.en
dc.description.sponsorshipUniversity of Ontario Institute of Technologyen
dc.identifier.urihttps://hdl.handle.net/10155/1287
dc.language.isoenen
dc.subjectVisualizationen
dc.subjectParallel Coordinates Ploten
dc.subjectPareto-fronten
dc.subjectSingle- and multioptimization algorithmsen
dc.subjectRadial Coordinate Visualizationen
dc.titleEnhancing parallel coordinates and RadVis visualizations using single-and multi-objective optimizationen
dc.typeThesisen
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorUniversity of Ontario Institute of Technology
thesis.degree.nameMaster of Applied Science (MASc)

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