Masters Theses

Abstract

"Text mining concerns discovering and extracting knowledge from unstructured data. It transforms textual data into a usable, intelligible format that facilitates classifying documents, finding explicit relationships or associations between documents, and clustering documents into categories. Given a collection of survey comments evaluating the civil engineering learning system, text mining technique is applied to discover and extract knowledge from the comments. This research focuses on the study of a systematic way to apply a software tool, SAS Enterprise Miner, to the survey data. The purpose is to categorize the comments into different groups in an attempt to identify "major" concerns from the users or students. Each group will be associated with a set of key terms. This is able to assist the evaluators of the learning system to obtain the ideas from those summarized terms without the need of going through a potentially huge amount of data"--Abstract, page iii.

Advisor(s)

Yu, Vincent (Wen-Bin)

Committee Member(s)

Luna, Ronaldo
Hall, Richard H.

Department(s)

Business and Information Technology

Degree Name

M.S. in Information Science and Technology

Sponsor(s)

National Science Foundation (U.S.)

Publisher

Missouri University of Science and Technology

Publication Date

Summer 2010

Pagination

ix, 76 pages

Rights

© 2010 Nitsawan Katerattanakul, All rights reserved.

Document Type

Thesis - Open Access

File Type

text

Language

English

Subject Headings

Data mining -- MethodologyDocument clusteringGeographic information systemsInformation retrieval -- Methodology

Thesis Number

T 9673

Print OCLC #

688641680

Electronic OCLC #

639933185

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