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    题名: A rough set-based association rule approach implemented on a brand trust evaluation model
    作者: Shu-Hsien Liao;Yin-Ju Chen
    关键词: Data mining;rough set theory;association rule;ratio scale data processing;brand trust evaluation model
    日期: 2016-12
    上传时间: 2017-03-24 02:11:05 (UTC+8)
    出版者: Taylor & Francis
    摘要: In commerce, businesses use branding to differentiate their product and service offerings from those of their competitors. The brand incorporates a set of product or service features that are associated with that particular brand name and identifies the product/service segmentation in the market. This study proposes a new data mining approach, a rough set-based association rule induction, implemented on a brand trust evaluation model. In addition, it presents as one way to deal with data uncertainty to analyse ratio scale data, while creating predictive if–then rules that generalise data values to the retail region. As such, this study uses the analysis of algorithms to find alcoholic beverages brand trust recall. Finally, discussions and conclusion are presented for further managerial implications.
    關聯: Journal of Experimental & Theoretical Artificial Intelligence 29(4), p.911–927
    DOI: 10.1080/0952813X.2016.1264089
    显示于类别:[管理科學學系暨研究所] 期刊論文

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