APPLYING MINING FUZZY ASSOCIATION RULES  TO SUPPORT STUDENTS IN STUDY PLANNING

Nguyễn Tuấn Anh; Trịnh Thúy Hà

APPLYING MINING FUZZY ASSOCIATION RULES TO SUPPORT STUDENTS IN STUDY PLANNING

About this article

Received: 01/06/20 Revised: 04/12/21 Published: 04/02/21

Authors

1. Nguyen Tuan Anh , Trường Đại học Công nghệ thông tin và Truyền thông - ĐH Thái Nguyên
2. Trinh Thuy Ha, TNU - University of Information and Communication Technology

Abstract

Fuzzy association rules have been investigated by many authors under several different approaches with worth results. The approaches of the published papers mostly used a single-granularity fuzzy set structure for fuzzy association rule. In this paper, we present the application of fuzzy association rule mining method using Hedge algebras in assisting students to plan their learning, fuzzy set structures of attributes are built on Hedge algebras. using multi-granularity representation. Using Hedge algebras to build fuzzy sets using a multi-granularity representation is much simpler than using fuzzy set theory. The advantage of using multi-granularity set structure helps us to explore fuzzy association rules that are both general and detailed. The test results were performed on a data set of 157 students in 10 core subjects. The experimental results showed that the key courses extracted by our proposed approach provide useful information to educational managers to improve the training efficiency. This results would help students to choose suitable subjects for the purpose of achieving high scores in study.

Keywords

Hedge algebras; Association rules; Data Mining; Fuzzy set; Course registration

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