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    Interesting Rule Induction Module: Adding Support for Unknown Attribute Values

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    Lindsey_ku_0099M_15002_DATA_1.pdf (191.2Kb)
    Implementation of IRIM (11.45Kb)
    Issue Date
    2016-12-31
    Author
    Lindsey, Theodore S.
    Publisher
    University of Kansas
    Format
    49 pages
    Type
    Thesis
    Degree Level
    M.S.
    Discipline
    Electrical Engineering & Computer Science
    Rights
    Copyright held by the author.
    Metadata
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    Abstract
    IRIM (Interesting Rule Induction Module) is a rule induction system designed to induce particularly strong, simple rule sets. Additionally, IRIM does not require prior discretization of numerical attribute values. IRIM does not necessarily produce consistent rules that fully describe the target concepts, however, the rules induced by IRIM often lead to novel revelations of hidden relationships in a dataset. In this paper, we attempt to extend the IRIM system to be able to handle missing attribute values (in particular, lost and do-not-care attribute values) more thoroughly than ignoring the cases that they belong to. Further, we include an implementation of IRIM in the modern programming language Python that has been written for easy inclusion in within a Python data mining package or library. The provided implementation makes use of the Pandas module which is built on top of a C back end for quick performance relative to the performance normally found with Python.
    URI
    http://hdl.handle.net/1808/24163
    Collections
    • Engineering Dissertations and Theses [1055]
    • Theses [3824]

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    Contact KU ScholarWorks
    785-864-8983
    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    785-864-8983

    KU Libraries
    1425 Jayhawk Blvd
    Lawrence, KS 66045
    Image Credits
     

     

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