AN APPLIED RESEARCH BASED ON ROUGH SET FOR DISCOVERING AND IMPROVING THE QUALITY OF THE ASSOCIATION RULES SET ON THE TEACHING AND LEARNING DATABASE AT NHA TRANG UNIVERSITY

Abstract

One of the important problems in rule-induction methods is how to extract interesting, relevant and novel rules. This paper presents an application of an evaluation technique based on Rough set theory which can help not only to reduce the number of rules, but also to extract higher quality rules. Rules generated from our Apriori-DT algorithm are evaluated for reducing and extracting higher quality rule set by applying a fertile method introduced by Jiye Lee et al. Experimental results on the teaching and learning database at Nha Trang University (TLNTU) illustrates the potential usefulness of this application in the education field.

Keywords: Rough Set; data mining; association rules; evaluation; rules-as-attributes measure; quality of teaching