Rough-Set-Based Association Rules Applied to Brand Trust Evaluation Model
學年 98
學期 1
出版（發表）日期 2010-01-01
作品名稱 Rough-Set-Based Association Rules Applied to Brand Trust Evaluation Model
作品名稱（其他語言）
著者 Liao, Shu-hsien; Lin, Hwei-jen
單位 淡江大學經營決策學系
出版者 Heidelberg: Springer
著錄名稱、卷期、頁數 Lecture Notes in Computer Science 6443,　p.634-641
摘要 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.
關鍵字 Machine Learning;Knowledge Representation;Knowledge-Based Systems;Rough sets; Association rules
語言 en
ISSN 1611-3349
期刊性質 國外
收錄於 EI
產學合作
通訊作者 Liao Shu-Hsien; Chen Yin-Ju; Chu, Pei-Hui
審稿制度 否
國別 DEU
公開徵稿
出版型式 ,電子版,紙本
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