. As the amount of electronic information increases, traditional lexical (or Boolean) information retrieval techniques will become less useful. Large, heterogeneous collections will be difficult to search since the sheer volume of unranked documents returned in response to a query will overwhelm the user. Vector-space approaches to information retrieval, on the other hand, allow the user to search for concepts rather than specific words and rank the results of the search according to their relative similarity to the query. One vector-space approach, Latent Semantic Indexing (LSI), has achieved up to 30% better retrieval performance than lexical searching techniques by employing a reduced-rank model of the term-document space. However, the original implementation of LSI lacked the execution efficiency required to make LSI useful for large data sets. A new implementation of LSI, LSI++, seeks to make LSI efficient, extensible, portable, and maintainable. The LSI++ Application Programming ...

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Acknowledgments I thank my advisor, Dr. Michael Berry, for his patience, encouragement, support, and advice throughout this project. I would also like to thank Dr. David Straight and Dr. Brad Vander Zanden for serving on my thesis committee.

Data Mining is the application of algorithms for extracting valuable information from large databases in order to make important business decisions. This study explores a new technique for data mining -- Latent Semantic Indexing (LSI). LSI is an efficient information retrieval method for textual documents. By determining the singular value decomposition (SVD) of a large sparse term-bydocument matrix, LSI constructs an approximate vector space model which represents important associative relationships between terms and documents that are not evident in individual documents. This thesis explores the applicability of the LSI model to numerical databases, especially consumer product data. By properly chosing attributes of data records as terms or documents, a term-by-document incidence matrix is built and then a distribution-based indexing scheme is employed to construct a correlated distribution matrix. Hence a similar LSI vector space model can be generated to detect useful or hidden pa...

the final electronic copy of this thesis for form and content and recommend that it be ac-