Thesis (M. S.)--Architecture, Georgia Institute of Technology, 2008. / Committee Chair: Spuybroek, Lars; Committee Member: Al-Haddad, Tristan; Committee Member: Romm, Stuart.

Thesis (Ph.D.)--University of Adelaide, School of Chemistry and Physics, Discipline of Physics, 2006. / Includes author's previously published papers. "February 2006" Bibliography: p. [129]-140. Also available in print form.

Thesis (Ph. D.)--Oregon State University, 1997. / Typescript (photocopy). Includes bibliographical references (leaves 105-107). Also available on the World Wide Web.

Thesis (Ph.D)--Materials Science and Engineering, Georgia Institute of Technology, 2009. / Committee Chair: Tannenbaum, Rina; Committee Member: Garmestani, Hamid; Committee Member: Jacob, Karl; Committee Member: Patterson, Tim; Committee Member: Singh, Preet. Part of the SMARTech Electronic Thesis and Dissertation Collection.

Thesis (Ph. D.)--Duke University, 2007. / Includes bibliographical references.

Thesis (M.S)--Ohio University, March, 2005. / Includes bibliographical references (p. 78-82)

Thesis (M.S)--Ohio University, March, 2005. / Title from PDF t.p. Includes bibliographical references (p. 78-82)

In this work, we utilize Self Organized Maps (SOM) to cluster and classify hospital related data with large dimensions, provided by Medicare website. These data have published every year and it includes numerous measures for each hospital in the nationwide. It might be possible to unearth some correlations in health-care industry by being able to interpreting this dataset, for example by examining the relations between data of immunizations department to readmission records and hospital expenses. It is not feasible to make any sense from these measures altogether using traditional methods (2D or 3D charts, diagrams or graphs, different tables), because as a result of being human, we cannot comprehend more than 3 dimensions with naked eyes. Since it would be very useful if we could correlate the dimensions to each other to discover new patterns and knowledge, SOMs are a type of Artificial Neural Networks that can be trained using unsupervised learning to illustrate complex and high dimensional data by generating a low dimension representation of the training sample. This way, a powerful and easy-to-interpret visualization will be provided for healthcare officials to rapidly identify the correlation between different attributes of the dataset using clusters illustration

<p> This investigation examines several methods designed to minimize the computational cost of retrieving records from a binary search tree.</p> <p> No knowledge of the probabilities with which these records are requested is assumed. The aim of each method is to gradually restructure an initial, arbitrary (and perhaps costly) tree into one which has minimal search cost, on the basis of experience.</p> <p> While no one such 'self-organizing' method has yet received theoretical substantiation, it is hoped that this empirial investigation may assist in this endeavour.</p> / Thesis / Master of Science (MSc)

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