【成果內容】

一、講　　題：

1. Mining fuzzy specific rare itemsets for education data

2. 期刊投稿經驗分享

二、主 講 人：翁政雄 老師

三、內容大綱：

1. Mining fuzzy specific rare itemsets for education data

Association rule mining is an important data analysis method for the discovery of associations within data. There have been many studies focused on finding fuzzy association rules from transaction databases. Unfortunately, in the real world, one may have available relatively infrequent data, as well as frequent data. From infrequent data, we can find a set of rare itemsets that will be useful for teachers to find out which students need extra help in learning. While the previous association rules discovery techniques are able to discover some rules based on frequency, this is insufficient to determine the importance of a rule composed of frequency-based data items. To remedy this problem, we develop a new algorithm based on the Apriori approach to mine fuzzy specific rare itemsets from quantitative data. Finally, fuzzy association rules can be generated from these fuzzy specific rare itemsets. The patterns are useful to discover learning problems. Experimental results show that the proposed approach is able to discover interesting and valuable patterns from the survey data.。

2.期刊投稿經驗分享

本次的講座活動圓滿地告一段落。經由講座之闡述，老師們更能瞭解在 balanced data 中的 rare itemsets 如何以 rank 定義 fuzzy specific rare itemsets 並符合 association rule 的準則。此結果對於教育輔導及偵測駭客入侵有重大貢獻。