|標題：Relative Association Rules Based on Rough Set Theory|
|作品名稱||Relative Association Rules Based on Rough Set Theory|
|著者||Liao, Shu-hsien; Chen, Yin-ju; Ho, Shiu-Hwei|
|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.