SILS Electronic Theses and Dissertations

Please use this identifier to cite or link to this item: http://hdl.handle.net/1901/208

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DC Field	Value	Language
contributor.advisor	Robert M. Losee	en
creator	Jonathan L. Elsas	en
date.accessioned	2005-07-06T19:04:23Z	-
date.available	2005-07-06	en
date.available	2005-07-06T19:04:23Z	-
date.issued	2005-07-06T19:04:23Z	-
date.submitted	July 6, 2005	en
identifier.uri	http://hdl.handle.net/1901/208	-
description.abstract	Dimensionality reduction in the bag-of-words vector space document representation model has been widely studied for the purposes of improving accuracy and reducing computational load of document retrieval tasks. These techniques, however, have not been studied to the same degree with regard to document clustering tasks. This study evaluates the effectiveness of two popular dimensionality reduction techniques for clustering, and their effect on discovering accurate and understandable topical groupings of documents. The two techniques studied are Latent Semantic Analysis and Independent Component Analysis, each of which have been shown to be effective in the past for retrieval purposes.	en
format	application/pdf	en
format.extent	1008122 bytes	-
format.mimetype	application/pdf	-
language.iso	en_US	en
publisher	School of Information and Library Science	en
subject	Information Retrieval, Statistical Methods/Evaluation	en
title	An Evaluation of Projection Techniques for Document Clustering: Latent Semantic Analysis and Independent Component Analysis	en
type	Electronic Theses and Dissertations	en
degree.discipline	Information Science	en
degree.grantor	University of North Carolina at Chapel Hill	en
degree.level	Master	en
degree.name	Master of Science	en
license	http://creativecommons.org/licenses/by-nc/1.0/	en
Appears in Collections:	SILS Master's Papers

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