Abstract
The clustering technique, which is widely employed for data analysis and data mining, represents the way of gathering similar objects or data with each other according to similar criteria in several clusters. Recently, meta-heuristic optimization techniques have become one of the most common approaches for researchers to solve clustering problems. We propose in this paper a novel algorithm for solving the data clustering problem based on a new optimization technique namely as Coyote Clustering Technique (CCT) inspired by the coyotes’ behaviors. We have studied the proposed method by applying a famous and widely set of data used in this field. The outputs of the proposed Technique proved their efficiency by recording good results in speed, accuracy, and stability.
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Mohamed, A., Saber, W., Elnahry, I., Hassanien, A.E. (2020). Clustering Analysis Based on Coyote Search Technique. In: Hassanien, AE., Azar, A., Gaber, T., Oliva, D., Tolba, F. (eds) Proceedings of the International Conference on Artificial Intelligence and Computer Vision (AICV2020). AICV 2020. Advances in Intelligent Systems and Computing, vol 1153. Springer, Cham. https://doi.org/10.1007/978-3-030-44289-7_18
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DOI: https://doi.org/10.1007/978-3-030-44289-7_18
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