Daniel L Pierce

US Patent:

6611825, Aug 26, 2003

Filed:

Jun 9, 1999

Appl. No.:

09/328888

Inventors:

D. Dean Billheimer - Seattle WA
Andrew James Booker - Seattle WA
Michelle Keim Condliff - Issaquah WA
Mark Thomas Greaves - Seattle WA
Fredrick Baden Holt - Bellevue WA
Anne Shu-Wan Kao - Bellevue WA
Daniel John Pierce - Woodinville WA
Stephen Robert Poteet - Bellevue WA
Yuan-Jye Wu - Issaquah WA

Assignee:

The Boeing Company - Seattle WA

International Classification:

G06N 500

US Classification:

706 45, 382156, 707 1

Abstract:

A text mining program is provided that allows a user to perform text mining operations, such as: information retrieval, term and document visualization, term and document clustering, term and document classification, summarization of individual documents and groups of documents, and document cross-referencing. This is accomplished by representing the text of a document collection using subspace transformations. This subspace transformation representation is performed by: constructing a term frequency matrix of the term frequencies for each of the documents, transforming the term frequencies for statistical purposes, and projecting the documents or the terms into a lower dimensional subspace. As the document collection is updated, the subspace is dynamically updated to reflect the new document collection.

US Patent:

6701305, Mar 2, 2004

Filed:

Oct 20, 2000

Appl. No.:

09/693114

Inventors:

Fredrick Baden Holt - Seattle WA
Anne Shu-Wan Kao - Bellevue WA
Daniel John Pierce - Woodinville WA
Stephen Robert Poteet - Bellevue WA
Yuan-Jye Wu - Issaquah WA

Assignee:

The Boeing Company - Seattle WA

International Classification:

G06F 1700

US Classification:

706 45, 706 12, 706 46

Abstract:

Methods, apparatus and computer program products are provided for retrieving information from a text data collection and for classifying a document into none, one or more of a plurality of predefined classes. In each aspect, a representation of at least a portion of the original matrix is projected into a lower dimensional subspace and those portions of the subspace representation that relate to the term(s) of the query are weighted following the projection into the lower dimensional subspace. In order to retrieve the documents that are most relevant with respect to a query, the documents are then scored with documents having better scores being of generally greater relevance. Alternatively, in order to classify a document, the relationship of the document to the classes of documents is scored with the document then being classified in those classes, if any, that have the best scores.