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    Rough sets for mining educational data

    190475_190475.pdf (377.8Kb)
    Access Status
    Open access
    Authors
    Johnson, J.
    Johnson, Genevieve
    Cavanagh, Rob
    Date
    2012
    Type
    Conference Paper
    
    Metadata
    Show full item record
    Citation
    Johnson, Julia Ann and Johnson, Genevieve Marie and Cavanagh, Robert F. 2012. Rough sets for mining educational data, in Proceedings of the 2012 Spring World Congress on Engineering and Technology (SCET 2012): Sharing, Cooperating and Improving, May 27-30 2012, pp. 131-134. Xi'an, China: Institute of Electrical and Electronic Engineers.
    Source Title
    Proceedings of the Spring World Congress on Engineering and Technology
    Source Conference
    2012 Spring World Congress on Engineering and Technology
    DOI
    10.1109/SCET.2012.6341891
    ISBN
    9781457719646
    Remarks

    NOTICE: This is the author’s version of a work in which changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication.

    Copyright © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

    URI
    http://hdl.handle.net/20.500.11937/43611
    Collection
    • Curtin Research Publications
    Abstract

    While educational data are typically analyzed with statistical software, data mining techniques are increasingly appropriate in revealing complex relationships among multiple variables in large amounts of data. We experimented with the rough set method in conjunction with statistical analysis to identify patterns in, and thereby extract meaning from, complex educational data. Results establish the benefits of combining rough set decision making with stochastic analysis in mining exceedingly complex and difficult to interpret educational data sets.

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