review · E3S Web of Conferences · 2023

Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review

Cited by 7Open access

Abstract

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.

Cite this paper

APA 7

Zakur, Y., & Flaih, L. (2023). Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review. E3S Web of Conferences, 448, 02021. https://doi.org/10.1051/e3sconf/202344802021

MLA 9

Zakur, Yahya, and Laith Flaih. “Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review.” E3S Web of Conferences, vol. 448, 2023, p. 02021, https://doi.org/10.1051/e3sconf/202344802021.

Chicago

Zakur, Yahya, and Laith Flaih. 2023. “Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review.” E3S Web of Conferences 448: 02021. https://doi.org/10.1051/e3sconf/202344802021.

Harvard

Zakur, Y. and Flaih, L. (2023) ‘Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review’, E3S Web of Conferences, 448, p. 02021. Available at: https://doi.org/10.1051/e3sconf/202344802021.

BibTeX

@article{zakur2023apriori,
  title = {{Apriori Algorithm and Hybrid Apriori Algorithm in the Data Mining: A Comprehensive Review}},
  author = {Zakur, Yahya and Flaih, Laith},
  journal = {E3S Web of Conferences},
  volume = {448},
  pages = {02021},
  year = {2023},
  doi = {10.1051/e3sconf/202344802021}
}

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