Penguins Search Optimisation Algorithm for Association Rules Mining

Youcef Gheraibia, Abdelouahab Moussaoui, Youcef Djenouri, Sohag Kabir, Peng Yeng Yin

Abstract


Association Rules Mining (ARM) is one of the most popular and well-known approaches for the decision-making process. All existing ARM algorithms are time consuming and generate a very large number of association rules with high overlapping. To deal with this issue, we propose a new ARM approach based on penguins search optimisation algorithm (Pe-ARM for short). Moreover, an efficient measure is incorporated into the main process to evaluate the amount of overlapping among the generated rules. The proposed approach also ensures a good diversification over the whole solutions space. To demonstrate the effectiveness of the proposed approach, several experiments have been carried out on different datasets and specifically on the biological ones. The results reveal that the proposed approach outperforms the well-known ARM algorithms in both execution time and solution quality.

ACM CCS (2012) Classification: Information systems → Information systems applications → Data mining → Association rules;
Theory of computation → Design and analysis of algorithms → Mathematical optimization → Mixed discrete-continuous optimization → Bio-inspired optimization

*To cite this article: Y. Gheraibia et al., "Penguins Search Optimisation Algorithm for Association Rules Mining", CIT. Journal of Computing and Information Technology, vol. 24, no. 2, pp. 165–179, 2016.


Keywords


association rules mining, penguins search optimisation algorithm, overlap measure, biological data-set, ARM

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DOI: https://doi.org/10.20532/cit.2016.1002745

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