A relative association rules based on rough set theory

淡江大學機構典藏 > 商管學院 > 管理科學學系暨研究所 > 會議論文 > Item 987654321/92057

jsp.display-item.identifier=請使用永久網址來引用或連結此文件: https://tkuir.lib.tku.edu.tw/dspace/handle/987654321/92057

题名:	A relative association rules based on rough set theory
作者:	Liao, Shu-hsien;Chung, Y.J.;Ho, S.H.
贡献者:	淡江大學管理科學學系
关键词:	Rough set;Data mining;Relative association rule;Ordinal data
日期:	2011-11
上传时间:	2013-08-12 16:26:47 (UTC+8)
摘要:	The traditional association rule that should be fixed in order to avoid the following: only trivial rules are retained and interesting rules are not discarded. In fact, the situations that use the relative comparison to express are more complete than those that use the absolute comparison. Through relative comparison, we proposes a new approach for mining association rule, which has the ability to handle uncertainty in the classing process, so that we can reduce information loss and enhance the result of data mining. In this paper, the new approach can be applied for finding association rules, which have the ability to handle uncertainty in the classing process, is suitable for interval data types, and help the decision to try to find the relative association rules within the ranking data.
關聯:	18th International Conference on Neural Information Processing 2011, (ICONIP2011)
DOI:	10.1007/978-3-642-24958-7_22
显示于类别:	[管理科學學系暨研究所] 會議論文

文件中的档案:

没有与此文件相关的档案.

在機構典藏中所有的数据项都受到原著作权保护.

TAIR相关文章