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dc.contributor.advisorPark, Haesun
dc.contributor.authorChoo, Jae gul
dc.date.accessioned2013-09-20T13:30:41Z
dc.date.available2013-09-20T13:30:41Z
dc.date.created2013-08
dc.date.issued2013-07-02
dc.date.submittedAugust 2013
dc.identifier.urihttp://hdl.handle.net/1853/49121
dc.description.abstractWith the increasing amount of collected data, large-scale high-dimensional data analysis is becoming essential in many areas. These data can be analyzed either by using fully computational methods or by leveraging human capabilities via interactive visualization. However, each method has its drawbacks. While a fully computational method can deal with large amounts of data, it lacks depth in its understanding of the data, which is critical to the analysis. With the interactive visualization method, the user can give a deeper insight on the data but suffers when large amounts of data need to be analyzed. Even with an apparent need for these two approaches to be integrated, little progress has been made. As ways to tackle this problem, computational methods have to be re-designed both theoretically and algorithmically, and the visual analytics system has to expose these computational methods to users so that they can choose the proper algorithms and settings. To achieve an appropriate integration between computational methods and visual analytics, the thesis focuses on essential computational methods for visualization, such as dimension reduction and clustering, and it presents fundamental development of computational methods as well as visual analytic systems involving newly developed methods. The contributions of the thesis include (1) the two-stage dimension reduction framework that better handles significant information loss in visualization of high-dimensional data, (2) efficient parametric updating of computational methods for fast and smooth user interactions, and (3) an iteration-wise integration framework of computational methods in real-time visual analytics. The latter parts of the thesis focus on the development of visual analytics systems involving the presented computational methods, such as (1) Testbed: an interactive visual testbed system for various dimension reduction and clustering methods, (2) iVisClassifier: an interactive visual classification system using supervised dimension reduction, and (3) VisIRR: an interactive visual information retrieval and recommender system for large-scale document data.
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.publisherGeorgia Institute of Technology
dc.subjectDimension reduction
dc.subjectClustering
dc.subjectHigh-dimensional data
dc.subjectVisualization
dc.subjectVisual analytics
dc.subject.lcshDimensional analysis
dc.subject.lcshData structures (Computer science)
dc.subject.lcshInformation visualization
dc.subject.lcshVisual analytics
dc.subject.lcshMathematical statistics Data processing
dc.titleIntegration of computational methods and visual analytics for large-scale high-dimensional data
dc.typeDissertation
dc.description.degreePh.D.
dc.contributor.departmentComputational Science and Engineering
thesis.degree.levelDoctoral
dc.contributor.committeeMemberStasko, John
dc.contributor.committeeMemberLebanon, Guy
dc.contributor.committeeMemberGray, Alexander
dc.contributor.committeeMemberWong, Pak
dc.date.updated2013-09-20T13:30:41Z


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