| Issue |
E3S Web Conf.
Volume 448, 2023
The 8th International Conference on Energy, Environment, Epidemiology and Information System (ICENIS 2023)
|
|
|---|---|---|
| Article Number | 02021 | |
| Number of page(s) | 9 | |
| Section | Information System | |
| DOI | https://doi.org/10.1051/e3sconf/202344802021 | |
| Published online | 17 November 2023 | |
Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review
1 University of Mazandaran, Faculty of Mathematical Sciences, 4741613534 Babolsar, Iran
2 Cihan University-Erbil, Department of Computer Sciences, 44001 Erbil, Iraq
* Corresponding author: yahyazakur92@gmail.com
Data mining has the potential to empower healthcare organizations by allowing them to analyze various aspects of patient information and discover connections between seemingly unrelated data. By harnessing advanced data analysis techniques, healthcare providers can identify trends in patients' medical conditions and behaviours. The Apriori algorithm is used for mining frequent item sets and devising association rules from a transactional database. The parameters “support” and “confidence” are used. Support refers to items’ frequency of occurrence; confidence is a conditional probability, while Apriori-Hybrid. Apriori-Hybrid is the combination of algorithms Apriori and Apriori-TID, which can classify large itemsets and can improve the accuracy of classification and it can also shed light on the basic mechanism. In this research, a comparison was made between the two algorithms in terms of capabilities, strengths, areas of use, and suggestions about the nature of using each algorithm.
Publisher note: The second affiliation address has been corrected from “Cihan University” to “Cihan University-Erbil”, on June 7, 2024.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.