教師資料查詢 | 類別: 期刊論文 | 教師: 廖述賢 LIAO SHU-HSIEN (瀏覽個人網頁)

標題:Relative Association Rules Based on Rough Set Theory
學年99
學期2
出版(發表)日期2011/06/01
作品名稱Relative Association Rules Based on Rough Set Theory
作品名稱(其他語言)
著者Liao, Shu-hsien; Chen, Yin-ju; Ho, Shiu-Hwei
單位淡江大學管理科學學系
出版者
著錄名稱、卷期、頁數Lecture notes in Computer Science 7063, pp.185-192
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.
關鍵字
語言英文(美國)
ISSN0302-9743
期刊性質國外
收錄於EI;
產學合作
通訊作者
審稿制度
國別德國
公開徵稿
出版型式,紙本
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