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    School of Information Studies, Syracuse University, Syracuse, NY
    Comparative Media Studies, Massachusetts Institute of Technology

    Bibliomining for automated collection development in a digital library setting: Using data mining to discover web-based scholarly research works

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    This 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 nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric 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.

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    Description

    Title : Bibliomining for automated collection development in a digital library setting: Using data mining to discover web-based scholarly research works
    Author(s) : Scott Nicholson
    Abstract : This 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 nonscholarly Web pages. Bibliomining, or data mining for libraries, was then used to create different classification models. Four techniques were used: logistic regression, nonparametric 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.
    Keywords : bibliomining, classification trees, computer programming languages, data mining, data structures, decision theory, digital libraries, intelligent agents, logistic regression, neural networks, nonparametric discriminant analysis, pattern matching computer

    Subject : unspecified
    Area : Other
    Language : English
    Year : 2003

    Affiliations School of Information Studies, Syracuse University, Syracuse, NY
    Journal : Journal of the American Society for Information Science and Tech
    Volume : 54
    Issue : 12
    Pages : 1081-1090
    Url : http://www.scopus.com/inward/record.url?eid=2-s2.0-0141860863&partnerID=40&md5=f32addffff53e420fc87ccfb2c2d93d7
    Doi : 10.1002/asi.

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