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    题名: A rough set-based association rule approach implemented on exploring beverages product spectrum
    作者: Liao, Shu-hsien;Chen, Yin-Ju
    贡献者: 淡江大學管理科學學系
    关键词: Data mining;Rough set;Association rule;Rough set association rule;Ordinal scale data;processing;Product spectrum
    日期: 2014-04-01
    上传时间: 2013-08-12 13:49:02 (UTC+8)
    出版者: Springer New York LLC
    摘要: When items are classified according to whether they have more or less of a characteristic, the scale used is referred to as an ordinal scale. The main characteristic of the ordinal scale is that the categories have a logical or ordered relationship to each other. Thus, the ordinal scale data processing is very common in marketing, satisfaction and attitudinal research. This study proposes a new data mining method, using a rough set-based association rule, to analyze ordinal scale data, which has the ability to handle uncertainty in the data classification/sorting process. The induction of rough-set rules is presented as method of dealing with data uncertainty, while creating predictive if—then rules that generalize data values, for the beverage market in Taiwan. Empirical evaluation reveals that the proposed Rough Set Associational Rule (RSAR), combined with rough set theory, is superior to existing methods of data classification and can more effectively address the problems associated with ordinal scale data, for exploration of a beverage product spectrum.
    關聯: Applied Intelligence 40(3), pp.464-478
    DOI: 10.1007/s10489-013-0465-1
    显示于类别:[管理科學學系暨研究所] 期刊論文

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