Thông tin tài liệu


Nhan đề : 
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
Năm xuất bản : 
2023
Nhà xuất bản : 
Springer
Tóm tắt : 
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.
Mô tả: 
CC BY
URI: 
https://link.springer.com/article/10.1007/s11227-022-04976-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7327
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