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Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining

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Abstract

The most challenging issues in association rule mining are dealing with numerical attributes and accommodating several criteria to discover optimal rules without any preprocessing activities or predefined parameter values. In order to deal with these problems, this paper proposes a multi-objective particle swarm optimization using an adaptive archive grid based on Pareto optimal strategy for numerical association rule mining. The proposed method aims to optimize confidence, comprehensibility and interestingness for rule discovery. By implementing this method, the numerical association rule does not require any major preprocessing activities such as discretization. Moreover, minimum support and confidence are not prerequisites. The proposed method is evaluated using three benchmark datasets containing numerical attributes. Furthermore, it is applied to a real case dataset taken from a weight loss application in order to discover association rules in terms of the behavior of customer page usage.

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Authors and Affiliations

  1. Department of Industrial Management, National Taiwan University of Science and Technology, No. 43, Section 4, Kee-Lung Road, Taipei, 106, Taiwan

    R. J. Kuo

  2. Data Science Division, Midtrans Corp., Kav. 10-11, Jend. Sudirman Road, Jakarta, 10220, Indonesia

    Monalisa Gosumolo

  3. Department of Logistics Engineering, Pertamina University, Teuku Nyak Arief Road, Simprug, Kebayoran Lama, Jakarta, 12220, Indonesia

    Ferani E. Zulvia

Authors
  1. R. J. Kuo
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  2. Monalisa Gosumolo
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  3. Ferani E. Zulvia
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Correspondence to Ferani E. Zulvia.

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Kuo, R.J., Gosumolo, M. & Zulvia, F.E. Multi-objective particle swarm optimization algorithm using adaptive archive grid for numerical association rule mining. Neural Comput & Applic 31, 3559–3572 (2019). https://doi.org/10.1007/s00521-017-3278-z

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  • DOI: https://doi.org/10.1007/s00521-017-3278-z

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