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 |
Bộ sưu tập | OER - Công nghệ thông tin |
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