Abstract
In this paper, the KFCM algorithm selects the Gaussian kernel function, maps the data into the high-dimensional feature space for clustering, and uses the optimal clustering center of the firefly algorithm as the initial value of KFCM, and then processes it through KFCM. Based on class analysis, a kernel fuzzy C-means clustering algorithm based on firefly algorithm (FA-KFCM) is proposed. Numerical experiments results show that FA-KFCM is superior to other algorithms in clustering accuracy and time efficiency.
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Acknowledgments
This work was supported by Inner Mongolia University for Nationalities Funds of China under Grant No. NMDYB18008.
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Cheng, C., Bao, C. (2019). A Kernel Fuzzy C-means Clustering Algorithm Based on Firefly Algorithm. In: Lu, H., Tang, H., Wang, Z. (eds) Advances in Neural Networks – ISNN 2019. ISNN 2019. Lecture Notes in Computer Science(), vol 11554. Springer, Cham. https://doi.org/10.1007/978-3-030-22796-8_49
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DOI: https://doi.org/10.1007/978-3-030-22796-8_49
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