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A modified multi-objective slime mould algorithm with orthogonal learning for numerical association rules mining

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Abstract

Association rule mining (ARM) is defined by its crucial role in finding common pattern in data mining. It has different types such as fuzzy, binary, numerical. In this paper, we introduce a multi-objective orthogonal mould algorithm (MOOSMA) with numerical association rule mining (NARM) which is a different type of ARM. Existing algorithms that deal with the NARM problem can be classified into three categories: distribution, discretization and optimization. The proposed approach belongs to the optimization category which is considered as a better way to deal with the problem. Our main objective is based on four efficiency measures related to each association: Support, Confidence, Comprehensibility, Interestingness. To test the performance of our approach, we started by testing our method on widely known generalized dynamic benchmark tests called CEC’09. This benchmark is composed of 20 test functions: 10 functions without constraints and 10 functions with constraints. Secondly, we applied our algorithm to solve NARM problem using 10 frequently used real-world datasets. Experimental analysis shows that our algorithm MOOSMA has better results in terms of Average Support, Average Confidence, Average Lift, Average Certain factor and Average Netconf. Note that source code of the MOOSMA algorithm is publicly available at https://github.com/gaithmanita/MOOSMA.

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Data availibility

The datasets generated during and/or analysed during the current study are available in the Function approximation repository of Bilkent University, http://funapp.cs.bilkent.edu.tr/DataSets.

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Authors and Affiliations

  1. Laboratory MARS, LR17ES05, ISITCom, University of Sousse, Sousse, Tunisia

    Salma Yacoubi, Ghaith Manita & Ouajdi Korbaa

  2. ESEN, University of Manouba, Manouba, Tunisia

    Ghaith Manita & Hamida Amdouni

  3. Laboratory RIADI, ENSI, University of Manouba, Manouba, Tunisia

    Hamida Amdouni

  4. Centre for Artificial Intelligence Research and Optimisation, Torrens University Australia, Brisbane, Australia

    Seyedali Mirjalili

  5. YFL (Yonsei Frontier Lab), Yonsei University, Seoul, Korea

    Seyedali Mirjalili

  6. ISITCom, University of Sousse, Hammam Sousse, Tunisia

    Ouajdi Korbaa

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  1. Salma Yacoubi
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Correspondence to Ghaith Manita.

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Yacoubi, S., Manita, G., Amdouni, H. et al. A modified multi-objective slime mould algorithm with orthogonal learning for numerical association rules mining. Neural Comput & Applic 35, 6125–6151 (2023). https://doi.org/10.1007/s00521-022-07985-w

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