A rough set-based association rule approach implemented on a brand trust evaluation model
學年 105
學期 1
出版（發表）日期 2016-12-26
作品名稱 A rough set-based association rule approach implemented on a brand trust evaluation model
作品名稱（其他語言）
著者 Shu-Hsien Liao; Yin-Ju Chen
單位
出版者
著錄名稱、卷期、頁數 Journal of Experimental & Theoretical Artificial Intelligence 29(4), p.911–927
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.
關鍵字 Data mining;rough set theory;association rule;ratio scale data processing;brand trust evaluation model
語言 en
ISSN 0952-813X; 1362-3079
期刊性質 國外
收錄於 SCI EI
產學合作
通訊作者 Shu-Hsien Liao
審稿制度 是
國別 GBR
公開徵稿
出版型式 ,電子版,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/110110 )

SDGS 產業創新與基礎設施