• Login
    View Item 
    •   Home
    • Colleges, Departments, and Organizations
    • Digital Library of Information Science & Technology (DLIST)
    • DLIST
    • View Item
    •   Home
    • Colleges, Departments, and Organizations
    • Digital Library of Information Science & Technology (DLIST)
    • DLIST
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UA Campus RepositoryCommunitiesTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournalThis CollectionTitleAuthorsIssue DateSubmit DateSubjectsPublisherJournal

    My Account

    LoginRegister

    About

    AboutUA Faculty PublicationsUA DissertationsUA Master's ThesesUA Honors ThesesUA PressUA YearbooksUA CatalogsUA Libraries

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    Document clustering for electronic meetings: an experimental comparison of two techniques

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    roussinov.pdf
    Size:
    1.852Mb
    Format:
    PDF
    Download
    Author
    Roussinov, Dmitri G.
    Chen, Hsinchun
    Issue Date
    1999-11
    Submitted date
    2004-09-04
    Keywords
    World Wide Web
    Classification
    Local subject classification
    National Science Digital Library
    NSDL
    Artificial intelligence lab
    AI lab
    Group decision support systems
    Text document clustering
    Empirical study
    Self-organizing maps
    Neural networks
    Cluster analysis
    
    Metadata
    Show full item record
    Citation
    Document clustering for electronic meetings: an experimental comparison of two techniques 1999-11, 27(1-2):67-80 Decision Support Systems
    Publisher
    Elsevier
    Journal
    Decision Support Systems
    Description
    Artificial Intelligence Lab, Department of MIS, University of Arizona
    URI
    http://hdl.handle.net/10150/105091
    Abstract
    In this article, we report our implementation and comparison of two text clustering techniques. One is based on Wardâ s clustering and the other on Kohonenâ s Self-organizing Maps. We have evaluated how closely clusters produced by a computer resemble those created by human experts. We have also measured the time that it takes for an expert to â â clean upâ â the automatically produced clusters. The technique based on Wardâ s clustering was found to be more precise. Both techniques have worked equally well in detecting associations between text documents. We used text messages obtained from group brainstorming meetings.
    Type
    Journal Article (Paginated)
    Language
    en
    Collections
    DLIST

    entitlement

     
    The University of Arizona Libraries | 1510 E. University Blvd. | Tucson, AZ 85721-0055
    Tel 520-621-6442 | repository@u.library.arizona.edu
    DSpace software copyright © 2002-2017  DuraSpace
    Quick Guide | Contact Us | Send Feedback
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.