Study methods of semantically expressing fuzzy concepts based the MF or other mathematical methods providing that they express the semantics of the most suitable concepts; studying methods of knowledge exploitation in general and fuzzy rules in particular; sesearch different data representations of information so that it can be exploited in ARs in a diverse and meaningful way. | MINISTRY OF EDUCATION VIETNAM ACADEMY OF AND TRAINING SCIENCE AND TECHNOLOGY GRADUATE UNIVERSITY OF SCIENCE AND TECHNOLOGY ------------------------------- NGUYEN TUAN ANH RESEARCH ON DEVELOPING METHOD OF MINING FUZZY ASSOCIATION RULES BASED ON LINGUISTIC INFORMATION AND ITS APPLICATION Major Mathematical Foundation for informatics Code 62 46 01 10 SUMMARY OF COMPUTER AND INFORMATION TECHNOLOGY DOCTORAL THESIS HA NOI 2020 List of works of author Trần Thái Sơn Nguyễn Tuấn Anh Nâng cao hiệu quả khai phá luật kết hợp mờ theo hướng tiếp cận đại số gia tử quot Kỷ yếu hội nghị quốc gia lần VI 1 về nghiên cứu cơ bản và ứng dụng công nghệ thông tin Fair - Huế 6 2013. Tran Thai Son Nguyen Tuan Anh Improve efficiency fuzzy association 2 rule using hedge algebra approach Journal of Computer Science and Cybernetics Vol 30 No 4 2014. Tran Thai Son Nguyen Tuan Anh Hedges Algebras and fuzzy partition 3 problem for qualitative attributes Journal of Computer Science and Cybernetics 2016. Tran Thai Son Nguyen Tuan Anh Partition fuzzy domain with multi- 4 granularity representation of data based on Hedge Algebra approach Journal of Computer Science and Cybernetics vol. 33 pp. 63-76 2017. 1 INTRODUCTION Nowadays there is an important research direction on which the problem of mining association rules AR is concerned and is soon developed in the direction of data mining. Specially many algorithms have been developed in different directions but mainly focused on two main directions i Improve the average speed of rule mining algorithms because this is an exponentially complex problem due to repeated database DB scans. ii Further research on the meaning of mining rules means as not all mining rules have implications for users. Fuzzy AR take the form quot If X is A then Y is B quot . quot X is A quot is called Premise quot Y is B quot is called the conclusion of the rule. 1 2 Y 1 2 is a subsection of a set of attributes I of the DB. 1 2 B 1 2 are the corresponding fuzzy sets of .
