Search

Current filters:

Current filters:

Author

Subject

Date issued

Has File(s)

Search Results

Results 1-1 of 1 (Search time: 0.0 seconds).
  • <<
  • 1
  • >>
    • CC-IFIM: an efficient approach for incremental frequent itemset mining based on closed candidates
    Authors: Maged, Magdy; Fayed F. M., Ghaleb; Dawlat A. El A., Mohamed;  Advisor: -;  Co-Author: - (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.