Integration of computational methods and visual analytics for large-scale high-dimensional data

Choo, Jae gul

Title:

Integration of computational methods and visual analytics for large-scale high-dimensional data

dc.contributor.advisor	Park, Haesun
dc.contributor.author	Choo, Jae gul
dc.contributor.committeeMember	Stasko, John
dc.contributor.committeeMember	Lebanon, Guy
dc.contributor.committeeMember	Gray, Alexander
dc.contributor.committeeMember	Wong, Pak
dc.contributor.department	Computational Science and Engineering
dc.date.accessioned	2013-09-20T13:30:41Z
dc.date.available	2013-09-20T13:30:41Z
dc.date.created	2013-08
dc.date.issued	2013-07-02
dc.date.submitted	August 2013
dc.date.updated	2013-09-20T13:30:41Z
dc.description.abstract	With 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.description.degree	Ph.D.
dc.format.mimetype	application/pdf
dc.identifier.uri	http://hdl.handle.net/1853/49121
dc.language.iso	en_US
dc.publisher	Georgia Institute of Technology
dc.subject	Dimension reduction
dc.subject	Clustering
dc.subject	High-dimensional data
dc.subject	Visualization
dc.subject	Visual analytics
dc.subject.lcsh	Dimensional analysis
dc.subject.lcsh	Data structures (Computer science)
dc.subject.lcsh	Information visualization
dc.subject.lcsh	Visual analytics
dc.subject.lcsh	Mathematical statistics Data processing
dc.title	Integration of computational methods and visual analytics for large-scale high-dimensional data
dc.type	Text
dc.type.genre	Dissertation
dspace.entity.type	Publication
local.contributor.advisor	Park, Haesun
local.contributor.corporatename	College of Computing
local.contributor.corporatename	School of Computational Science and Engineering
relation.isAdvisorOfPublication	92013a6f-96b2-4ca8-9ef7-08f408ec8485
relation.isOrgUnitOfPublication	c8892b3c-8db6-4b7b-a33a-1b67f7db2021
relation.isOrgUnitOfPublication	01ab2ef1-c6da-49c9-be98-fbd1d840d2b1
thesis.degree.level	Doctoral