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CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates

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Bibliographic Details
Main Authors: Maged, Magdy, Fayed F. M., Ghaleb, Dawlat A. El A., Mohamed
Format: Book
Language:English
Published: Springer 2023
Subjects:
FIM
via incremental frequent itemset mining
Online Access:https://link.springer.com/article/10.1007/s11227-022-04976-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7327
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spelling oai:localhost:PNK-73272023-03-30T04:02:23Z CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates Maged, Magdy Fayed F. M., Ghaleb Dawlat A. El A., Mohamed FIM via incremental frequent itemset mining CC BY 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. 2023-03-30T04:02:23Z 2023-03-30T04:02:23Z 2023 Book https://link.springer.com/article/10.1007/s11227-022-04976-5 https://dlib.phenikaa-uni.edu.vn/handle/PNK/7327 en application/pdf Springer
institution Digital Phenikaa
collection Digital Phenikaa
language English
topic FIM
via incremental frequent itemset mining
spellingShingle FIM
via incremental frequent itemset mining
Maged, Magdy
Fayed F. M., Ghaleb
Dawlat A. El A., Mohamed
CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
description CC BY
format Book
author Maged, Magdy
Fayed F. M., Ghaleb
Dawlat A. El A., Mohamed
author_facet Maged, Magdy
Fayed F. M., Ghaleb
Dawlat A. El A., Mohamed
author_sort Maged, Magdy
title CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
title_short CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
title_full CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
title_fullStr CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
title_full_unstemmed CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
title_sort cc-ifim: an efficient approach for incremental frequent itemset mining based on closed candidates
publisher Springer
publishDate 2023
url https://link.springer.com/article/10.1007/s11227-022-04976-5
https://dlib.phenikaa-uni.edu.vn/handle/PNK/7327
_version_ 1761821913522372608
score 8.891145

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