Abstract
Gradual pattern extraction is a field in Knowledge Discovery in Databases that maps correlations between attributes of a data set as gradual dependencies. A gradual dependency may take the form: “the more Attribute\(_{K}\), the less Attribute\(_{L}\)”. Classical approa-ches for extracting gradual patterns extend either a breath-first search or a depth-first search strategy. However, these strategies can be computationally expensive and inefficient especially when dealing with large data sets. In this study, we investigate 3 population-based optimization techniques (i.e. ant colony optimization, genetic algorithm and particle swarm optimization) that may be employed improve the efficiency of mining gradual patterns. We show that ant colony optimization technique is better suited for gradual pattern mining task than the other 2 techniques. Through computational experiments on real-world data sets, we compared the computational performance of the proposed algorithms that implement the 3 population-based optimization techniques to classical algorithms for the task of gradual pattern mining and we show that the proposed algorithms outperform their classical counterparts.
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Acknowledgements
The authors would like to thank the French Government through the office of Co-operation and Cultural Service (Kenya) and the office of Campus France (Montpellier) for their involvement in creating the opportunity for this work to be produced. This work has been realized with the support of the High Performance Computing Platform: MESO@LR, financed by the Occitanie / Pyrénées-Méditerranée Region, Montpellier Mediterranean Metropole and Montpellier University.
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Owuor, D.O., Runkler, T., Laurent, A. et al. Ant colony optimization for mining gradual patterns. Int. J. Mach. Learn. & Cyber. 12, 2989–3009 (2021). https://doi.org/10.1007/s13042-021-01390-w
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DOI: https://doi.org/10.1007/s13042-021-01390-w