Thông tin chung
  English
  Đề tài NC khoa học
  Bài báo, báo cáo khoa học
  Hướng dẫn Sau đại học
  Sách và giáo trình
  Các học phần và môn giảng dạy
  Giải thưởng khoa học, Phát minh, sáng chế
  Khen thưởng
  Thông tin khác
  Tài liệu tham khảo
  Hiệu chỉnh
 
Số người truy cập: 108,304,320

 Khai phá tập mục thường xuyên song song bằng phương pháp automata cellular
Tác giả hoặc Nhóm tác giả: Trinh T.T.Tran, Thuan T.Nguyen, Giang L.Nguyen, Chau N.Truong
Nơi đăng: Journal of Computer Science and Cybernetics; Số: V.38-N.4;Từ->đến trang: 293-310;Năm: 2022
Lĩnh vực: Khoa học công nghệ; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for fuzzy association rule mining in the context of data mining. If frequent fuzzy item sets are detected, the decision-making process and formulating strategies in businesses will be made more precise. Because of the characteristics of these data models is the large number of transactions, unlimited and high-speed productions. This leads to limitations in calculating the support for item sets containing fuzzy attributes. As a result, mining using parallel processing techniques has emerged as a potential solution to the issue of slow availability. This study presents a reinforced technique for mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstrate that frequent set mining can be accomplished with less running time when the proposed method is compared to iMFFP and NPSFF methods.
ABSTRACT
Finding frequent fuzzy itemsets in operational quantitative databases is a significant challenge for fuzzy association rule mining in the context of data mining. If frequent fuzzy item sets are detected, the decision-making process and formulating strategies in businesses will be made more precise. Because of the characteristics of these data models is the large number of transactions, unlimited and high-speed productions. This leads to limitations in calculating the support for item sets containing fuzzy attributes. As a result, mining using parallel processing techniques has emerged as a potential solution to the issue of slow availability. This study presents a reinforced technique for mining frequent fuzzy sets based on cellular learning automata (CLA). The results demonstrate that frequent set mining can be accomplished with less running time when the proposed method is compared to iMFFP and NPSFF methods.
© Đại học Đà Nẵng
 
 
Địa chỉ: 41 Lê Duẩn Thành phố Đà Nẵng
Điện thoại: (84) 0236 3822 041 ; Email: dhdn@ac.udn.vn