NumAssociationRules.jl is a Julia framework for mining numerical association rules based on nature-inspired algorithms for optimization. Inspired by the NiaARM package, this package brings the power of numerical association rule mining to the Julia ecosystem.
The current version includes (but is not limited to) the following functions:
Ideas are based on the following research papers:
[1] Stupan, Ž., & Fister Jr., I. (2022). NiaARM: A minimalistic framework for Numerical Association Rule Mining. Journal of Open Source Software, 7(77), 4448.
[2] I. Fister Jr., A. Iglesias, A. Gálvez, J. Del Ser, E. Osaba, I Fister. Differential evolution for association rule mining using categorical and numerical attributes In: Intelligent data engineering and automated learning - IDEAL 2018, pp. 79-88, 2018.
[3] I. Fister Jr., V. Podgorelec, I. Fister. Improved Nature-Inspired Algorithms for Numeric Association Rule Mining. In: Vasant P., Zelinka I., Weber GW. (eds) Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol 1324. Springer, Cham.
[4] I. Fister Jr., I. Fister A brief overview of swarm intelligence-based algorithms for numerical association rule mining. arXiv preprint arXiv:2010.15524 (2020).
This package is distributed under the MIT License. This license can be found online at http://www.opensource.org/licenses/MIT.
This framework is provided as-is, and there are no guarantees that it fits your purposes or that it is bug-free. Use it at your own risk!