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
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,　p.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 Rough set;Data mining;Relative association rule;Ordinal data
語言 en_US
ISSN 0302-9743
期刊性質 國外
收錄於 EI
產學合作
通訊作者
審稿制度 否
國別 DEU
公開徵稿
出版型式 ,紙本
相關連結

機構典藏連結 ( http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/91972 )

機構典藏連結
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