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    Please use this identifier to cite or link to this item: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/65018


    Title: Rough-Set-Based Association Rules Applied to Brand Trust Evaluation Model
    Authors: Liao, Shu-hsien;Lin, Hwei-jen
    Contributors: 淡江大學經營決策學系
    Keywords: Face recognition;principle component analysis;PCA;two dimensional principle component analysis;2DPCA;discrete cosine transformation;DCT;weighted voting;spatial domain;frequency domain;genetic algorithms
    Date: 2010
    Issue Date: 2013-03-12 11:00:49 (UTC+8)
    Publisher: Heidelberg: Springer
    Abstract: The Internet has emerged as the primary database, and technological platform for electronic business (EB), including the emergence of online retail concerns. Knowledge collection, verification, distribution, storage, and re-use are all essential elements in retail. They are required for decision-making or problem solving by expert consultants, as well as for the accumulation of customers and market knowledge for use by managers in their attempts to increase sales. Previous data mining algorithms usually assumed that input data was precise and clean, this assumes would be eliminated if the best rule for each particular situation. The Algorithm we used in this study however, proved to function even when the input data was vague and unclean. We provided an assessment model of brand trust as an example, to show that the algorithm was able to provide decision makers additional reliable information, in the hope of building a rough set theoretical model and base of resources that would better suit user demand.
    Relation: Lecture Notes in Computer Science 6443, p.634-641
    DOI: 10.1007/978-3-642-16732-4_53
    Appears in Collections:[管理科學學系暨研究所] 期刊論文

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