A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application-Reference-Cited by-同舟云学术

A novel algorithm for mining couples of enhanced association rules based on the number of output couples and its application

Published:2023-11-01 Issue: Volume: Page:
ISSN:0925-9902
Container-title:Journal of Intelligent Information Systems
language:en
Short-container-title:J Intell Inf Syst

Author:

Máša Petr^ORCID,Rauch Jan^ORCID

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Hardware and Architecture,Information Systems,Software

Link

https://link.springer.com/content/pdf/10.1007/s10844-023-00820-1.pdf

Reference50 articles.

1. Agrawal, R., & Srikant, R.(1994). Fast algorithms for mining association rules in large databases. In 20th International conference on very large data bases, (pp. 487–499). San Francisco: VLDB ’94, Morgan Kaufmann Publishers Inc. http://dl.acm.org/citation.cfm?id=645920.672836

2. Agrawal, R., Imielinski, T., & Swami, A. N. (1993). Mining association rules between sets of items in large databases. In Proceedings of the 1993 ACM SIGMOD International conference on management of data, Washington, DC, USA, May 26-28, (pp. 207–216). https://doi.org/10.1145/170035.170072.

3. Aqra, I., Herawan, T., Abdul Ghani, N., Akhunzada, A., Ali, A., Bin Razali, R., Ilahi, M., & Raymond Choo, K. K. (2018). A novel association rule mining approach using tid intermediate itemset. PLOS ONE, 13(1), 1–32. https://doi.org/10.1371/journal.pone.0179703

4. Atzmueller, M. (2015). Subgroup discovery. WIREs Data Mining and Knowledge Discovery, 5(1), 35–49. https://doi.org/10.1002/widm.1144

5. Hahsler, M. (2023). ARULESPY: Exploring Association Rules and Frequent Itemsets in Python