Show simple item record

dc.contributor.authorNicholson, Scott
dc.date.accessioned2004-12-11T00:00:01Z
dc.date.available2010-06-18T23:48:51Z
dc.date.issued2003-12en_US
dc.date.submitted2004-12-11en_US
dc.identifier.citationBibliomining for Automated Collection Development in a Digital Library Setting: Using Data Mining to Discover Web-Based Scholarly Research Works 2003-12, 54(12) Journal of the American Society for Information Science and Technologyen_US
dc.identifier.urihttp://hdl.handle.net/10150/106521
dc.descriptionBased off Nicholson's 2000 University of North Texas dissertation, "CREATING A CRITERION-BASED INFORMATION AGENT THROUGH DATA MINING FOR AUTOMATED IDENTIFICATION OF SCHOLARLY RESEARCH ON THE WORLD WIDE WEB" located at http://scottnicholson.com/scholastic/finaldiss.docen_US
dc.description.abstractThis research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based on facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and non-scholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, non-parametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model on test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Web-based scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.
dc.format.mimetypetext/htmlen_US
dc.language.isoenen_US
dc.subjectWeb Miningen_US
dc.subjectData Miningen_US
dc.subjectDigital Librariesen_US
dc.subject.otherdigital librariesen_US
dc.subject.othercollection developmenten_US
dc.subject.otherWorld Wide Weben_US
dc.subject.othersearch enginesen_US
dc.subject.otherbibliominingen_US
dc.subject.otherdata miningen_US
dc.subject.otherintelligent agentsen_US
dc.titleBibliomining for Automated Collection Development in a Digital Library Setting: Using Data Mining to Discover Web-Based Scholarly Research Worksen_US
dc.typeJournal (On-line/Unpaginated)en_US
dc.identifier.journalJournal of the American Society for Information Science and Technologyen_US
refterms.dateFOA2018-06-12T13:11:08Z
html.description.abstractThis research creates an intelligent agent for automated collection development in a digital library setting. It uses a predictive model based on facets of each Web page to select scholarly works. The criteria came from the academic library selection literature, and a Delphi study was used to refine the list to 41 criteria. A Perl program was designed to analyze a Web page for each criterion and applied to a large collection of scholarly and non-scholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, non-parametric discriminant analysis, classification trees, and neural networks. Accuracy and return were used to judge the effectiveness of each model on test datasets. In addition, a set of problematic pages that were difficult to classify because of their similarity to scholarly research was gathered and classified using the models. The resulting models could be used in the selection process to automatically create a digital library of Web-based scholarly research works. In addition, the technique can be extended to create a digital library of any type of structured electronic information.


Files in this item

Thumbnail
Name:
asisdiss.html
Size:
214.1Kb
Format:
HTML

This item appears in the following Collection(s)

Show simple item record