DUYỆT THEO

HỒ SƠ TÁC GIẢ

Tìm kiếm theo: Tác giả Maged, Magdy

Duyệt theo: 0-9 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z
Hoặc nhập chữ cái đầu tiên:  
Kết quả [1 - 1] / 1
    • CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
    Tác giả : Maged, Magdy; Fayed F. M., Ghaleb; Dawlat A. El A., Mohamed;  Người hướng dẫn: -;  Đồng tác giả: - (2023)

    Frequent itemset mining (FIM) is the crucial task in mining association rules that finds all frequent k-itemsets in the transaction dataset from which all association rules are extracted. In the big-data era, the datasets are huge and rapidly expanding, so adding new transactions as time advances results in periodic changes in correlations and frequent itemsets present in the dataset. Re-mining the updated dataset is impractical and costly. This problem is solved via incremental frequent itemset mining.