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Methods and Tools for Visual Analytics.

dc.contributor.authorZhou, Haoen_US
dc.date.accessioned2012-01-26T20:02:15Z
dc.date.availableNO_RESTRICTIONen_US
dc.date.available2012-01-26T20:02:15Z
dc.date.issued2011en_US
dc.date.submitteden_US
dc.identifier.urihttps://hdl.handle.net/2027.42/89683
dc.description.abstractTechnological advances have led to a proliferation of data characterized by a complex structure; namely, high-dimensional attribute information complemented by relationships between the objects or even the attributes. Classical data mining techniques usually explore the attribute space, while network analytic techniques focus on the relationships, usually expressed in the form of a graph. However, visualization techniques offer the possibility to gain useful insight through appropriate graphical displays coupled with data mining and network analytic techniques. In this thesis, we study various topics of the visual analytic process. Specifically, in chapter 2, we propose a visual analytic algebra geared towards attributed graphs. The algebra defines a universal language for graph data manipulations during the visual analytic process and allows documentation and reproducibility. In chapter 3, we extend the algebra framework to address the uncertain querying problem. The algebra's operators are illustrated on a number of synthetic and real data sets, implemented in an existing visualization system (Cytoscape) and validated through a small user study. In chapter 4, we introduce a dimension reduction technique that through a regularization framework incorporates network information either on the objects or the attributes. The technique is illustrated on a number of real world applications. Finally, in the last part of the thesis, we present a multi-task generalized linear model that improves the learning of a single task (problem) by utilizing information from connected/similar tasks through a shared representation. We present an algorithm for estimating the parameters of the problem efficiently and illustrate it on a movie ratings data set.en_US
dc.language.isoen_USen_US
dc.subjectVisual Analyticsen_US
dc.titleMethods and Tools for Visual Analytics.en_US
dc.typeThesisen_US
dc.description.thesisdegreenamePhDen_US
dc.description.thesisdegreedisciplineStatisticsen_US
dc.description.thesisdegreegrantorUniversity of Michigan, Horace H. Rackham School of Graduate Studiesen_US
dc.contributor.committeememberMichailidis, Georgeen_US
dc.contributor.committeememberJagadish, Hosagrahar V.en_US
dc.contributor.committeememberShedden, Kerby A.en_US
dc.contributor.committeememberZhu, Jien_US
dc.subject.hlbsecondlevelStatistics and Numeric Dataen_US
dc.subject.hlbtoplevelScienceen_US
dc.description.bitstreamurlhttp://deepblue.lib.umich.edu/bitstream/2027.42/89683/1/zhouhao_1.pdf
dc.owningcollnameDissertations and Theses (Ph.D. and Master's)


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