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  • 标题:USING FUZZY SOFT SET ASSOCIATION RULE MINING APPROACH TO IDENTIFY THE STUDENT SKILL DATA ASSOCIATION
  • 本地全文:下载
  • 作者:DEDE ROHIDIN ; NOOR A. SAMSUDIN ; MOHD F. AB. AZIZ
  • 期刊名称:Journal of Theoretical and Applied Information Technology
  • 印刷版ISSN:1992-8645
  • 电子版ISSN:1817-3195
  • 出版年度:2019
  • 卷号:97
  • 期号:13
  • 页码:3691-3701
  • 出版社:Journal of Theoretical and Applied
  • 摘要:Graduates shall see the importance of improving their skills to prepare themselves to get the right jobs. Considering such important requirements, therefore many universities have designed various skill development programs in their curriculum. The students enrol in these programs and eventually, their skills performance will be evaluated using certain scores. This research does not aim to calculate the scores. Instead, we focus on how to find the relationship between parameters presented for the evaluation. In this study, we use the fuzzy-soft-association-rule mining (FSAR) approach and proposed the fast algorithm for finding association rule on fuzzy soft set. FSAR is a tool that combines Fuzzy Soft Set concepts and Association Rule Mining. We found that FSAR is an effective method to describe the relationship between parameters in large size data. Using FSAR, we will find a significant parameter or uncorrelated parameters for further analysis. This study recommends the selected parameters for determining the type of training for students. By selecting the right training, the University can reduce training cost significantly.
  • 关键词:Association Rules; Soft Set; Fuzzy-Soft-Association Rule
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