A rough set-based association rule approach implemented on exploring beverages product spectrum
學年 102
學期 2
出版（發表）日期 2014-04-01
作品名稱 A rough set-based association rule approach implemented on exploring beverages product spectrum
作品名稱（其他語言）
著者 Liao, Shu-hsien; Chen, Yin-Ju
單位 淡江大學管理科學學系
出版者 New York: Springer New York LLC
著錄名稱、卷期、頁數 Applied Intelligence 40(3), pp.464-478
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.
關鍵字 Data mining;Rough set;Association rule;Rough set association rule;Ordinal scale data;processing;Product spectrum
語言 en_US
ISSN 0924-669X;1573-7497
期刊性質 國外
收錄於 SCI
產學合作
通訊作者 Liao, Shu-hsien
審稿制度 是
國別 USA
公開徵稿
出版型式 ,電子版,紙本
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機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/91986 )

機構典藏連結
SDGS 產業創新與基礎設施