Rough-Set-Based Association Rules Applied to Brand Trust Evaluation Model

doi:10.1007/978-3-642-02568-6_9

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題名:	Rough-Set-Based Association Rules Applied to Brand Trust Evaluation Model
作者:	Liao, Shu-hsien;Lin, Hwei-jen
貢獻者:	淡江大學經營決策學系
關鍵詞:	Machine Learning;Knowledge Representation;Knowledge-Based Systems;Rough sets;Association rules
日期:	2010
上傳時間:	2013-03-12 11:00:49 (UTC+8)
出版者:	Heidelberg: Springer
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.
關聯:	Lecture Notes in Computer Science 6443,　p.634-641
DOI:	10.1007/978-3-642-02568-6_9
顯示於類別:	[管理科學學系暨研究所] 期刊論文 [資訊工程學系暨研究所] 期刊論文

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