Abstract
The swarm intelligence algorithms are established as comprehensive approaches for resolving the complicated problems in optimization through the simulation of “behaviors of the biological swarms.” Recently, data mining areas seek more attention that requires analysis of huge data and quick management. Most of the traditional techniques have not provided effective performance, and they are only applicable for differentiable and continuous functions. Further, the group of population-based techniques is proved to be effective in certain researches, where the swarm intelligence algorithms provide the significant potential for relevant data mining tasks. Hence, this paper demonstrates the biological inspiration and some of the swarm intelligence concepts, in which the main focus is given to “Particle Swarm Optimization (PSO) and Ant Colony Optimization (ACO) algorithms.” The primary data mining strategies are also described along with certain existing and present works based on swarm intelligence approaches.
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Murthy, M.Y.B., Prasanthi, K., Kilaru, M., Kakarla, S.R. (2023). Swarm Intelligence and Its Impact on Data Mining and Knowledge Discovery. In: Kumar, A., Senatore, S., Gunjan, V.K. (eds) ICDSMLA 2021. Lecture Notes in Electrical Engineering, vol 947. Springer, Singapore. https://doi.org/10.1007/978-981-19-5936-3_65
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DOI: https://doi.org/10.1007/978-981-19-5936-3_65
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