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Toward Reusing the Numerical Association Rule Mining Models

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Part of the Advances in Intelligent Systems and Computing book series (AISC,volume 1401)


Nowadays, it is no secret that modern machine learning methods are amongst the more computationally-intensive learning methods. The rise in the applications of computationally-intensive deep learning, automated machine learning methods, and even metaheuristics for optimization, have increased the consumption of electrical energy dramatically. Consequently, we can predict that the numbers of global carbon footprints, arising as a byproduct of the increased consumption of electrical energy during intensive computation, will be higher and higher in the near future. Fortunately, the research community is aware of these problems, and is, thus, looking for solutions of how to reduce the carbon footprint in the sense of the so-called Green AI. In line with this, the paper introduces a reusable model for Numerical Association Rule Mining which is also one of the hardest computationally-intensive learning methods. In the classical Numeric Association Rule Mining, the model (i.e., the archive of mined association rules) is created anew, when the new incoming association rule are emerging. The proposed reusable model only modifies the existing model accordingly by incoming new rules, while the complexity of computation decreases remarkably.


  • Association rule mining
  • Numerical association rule mining
  • Modeling
  • Metaheuristics
  • Green AI

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A. Galvez and A. Iglesias thank the European Union’s Horizon 2020 project PDE-GIR under the MSCA-RISE grant agreement No 778035, and the national project #TIN2017-89275-R from the Spanish Ministry of Science, Innovation and Universities (Computer Science National Program), Agencia Estatal de In- vestigación and European Funds FEDER (AEI/FEDER, UE). I. Fister and I. Fister Jr. thank the financial support from the Slovenian Research Agency (Re- search Core Funding No. P2-0042 and No. P2-0057, respectively).

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Correspondence to Iztok Fister Jr. .

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Fister, I., Iglesias, A., Galvez, A., Fister, I. (2022). Toward Reusing the Numerical Association Rule Mining Models. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham.

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