A relative association rules based on rough set theory
學年 100
學期 1
發表日期 2011-11-14
作品名稱 A relative association rules based on rough set theory
作品名稱（其他語言）
著者 Liao, Shu-hsien; Chung, Y.J.; Ho, S.H.
作品所屬單位 淡江大學管理科學學系
出版者
會議名稱 18th International Conference on Neural Information Processing 2011, (ICONIP2011)
會議地點 Shanghai, China
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
收錄於
會議性質 國際
校內研討會地點 無
研討會時間 20111114~20111117
通訊作者
國別 CHN
公開徵稿 Y
出版型式
出處 18th International Conference on Neural Information Processing 2011, (ICONIP2011)
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