Thesis (Ph.D.)--Duquesne University, 2004. / Title from document title page. Abstract included in electronic submission form. Includes bibliographical references (p. 204-209 ) and index.

Thesis (Ph. D.)--Illinois State University, 2003. / Title from title page screen, viewed Dec. 16, 2004. Dissertation Committee: Dissertation Committee: Edward R. Hines (chair), Kenneth H. Strand, Ross A. Hodel, Zeng Lin. Includes bibliographical references (leaves 79-83) and abstract. Also available in print.

Thesis (Ph. D.)--University of Hong Kong, 1995. / Includes bibliographical references.

Thesis (Ph. D.)--University of Hawaii at Manoa, 2004. / Includes bibliographical references (leaves 86-97).

Thesis (Ed. D.)--Virginia Polytechnic Institute and State University, 1993. / Vita. Abstract. Includes bibliographical references (leaves 105-112). Also available via the Internet.

Thesis (M.Med.Sc.)--University of Hong Kong, 2003. / Includes bibliographical references (leaves 77-89). Also available in print.

Thesis (M.A.)--Regis University, Denver, Colo., 2009. / Title from PDF title page (viewed on Jun. 29, 2010). Includes bibliographical references.

Thesis (Ph.D.) -- University of Rhode Island, 2009. / Typescript. Includes bibliographical references (leaves 150-165).

Thesis (M.Ost.)--Unitec New Zealand, 2009. / Includes bibliographical references (leaves 81-87)

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