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    Mining value-based item packages - An integer programming approach

    Access Status
    Fulltext not available
    Authors
    Achuthan, Narasimaha
    Gopalan, Raj
    Rudra, Amit
    Date
    2006
    Type
    Book Chapter
    
    Metadata
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    Citation
    Achuthan, Narasimaha and Gopalan, Raj and Rudra, Amit. 2006. Mining value-based item packages - An integer programming approach. ed. Carbonell, J.G. and Siekmann, J., 78-89. Heidelberg, Germany: Springer-Verlag.
    Source Title
    Lecture Notes in Artifical Intelligence (LNAI 3755): Data Mining
    DOI
    10.1007/11677437_7
    Faculty
    Department of Mathematics and Statistics
    Division of Engineering, Science and Computing
    Faculty of Science
    Remarks

    The original publication is available at http://www.springerlink.com

    URI
    http://hdl.handle.net/20.500.11937/44572
    Collection
    • Curtin Research Publications
    Abstract

    Traditional methods for discovering frequent patterns from large databases assume equal weights for all items of the database. In the real world, managerial decisions are based on economic values attached to the item sets. In this paper, we first introduce the concept of the value based frequent item packages problems. Then we provide an integer linear programming (ILP) model for value based optimization problems in the context of transaction data. The specific problem discussed in this paper is to find an optimal set of item packages (or item sets making up the whole transaction) that returns maximum profit to the organization under some limited resources. The specification of this problem allows us to solve a number of practical decision problems, by applying the existing and new ILP solution techniques. The model has been implemented and tested with real life retail data. The test results are reported in the paper.

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