| Autor: |
Arthur Yosef, Idan Roth, Eli Shnaider, Amos Baranes, Moti Schneider |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
Computers, Vol 13, Iss 3, p 62 (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2073-431X |
| DOI: |
10.3390/computers13030062 |
| Popis: |
Association rule learning is a machine learning approach aiming to find substantial relations among attributes within one or more datasets. We address the main problem of this technology, which is the excessive computation time and the memory requirements needed for the processing of discovering the association rules. Most of the literature pertaining to the association rules deals extensively with these issues as major obstacles, especially for very large databases. In this paper, we introduce a method that requires substantially lowers the run time and memory requirements in comparison to the methods presently in use (reduction from O(2m) to O2m2 in the worst case). |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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