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.
| Identifer | oai:union.ndltd.org:UNC_CH/oai:etd.ils.unc.edu:1901/208 |
| Date | 6 July 2005 |
| Creators | Jonathan L. Elsas |
| Contributors | Robert M. Losee |
| Publisher | School of Information and Library Science |
| Source Sets | University of North Carolina-Chapel Hill |
| Language | en_US |
| Detected Language | English |
| Type | Electronic Theses and Dissertations |
| Format | application/pdf, 1008122 bytes, application/pdf |
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