Abstract
Artificial Bee Colony (ABC) algorithm, which simulates the intelligent foraging behavior of a honey bee swarm, is one of optimization algorithms introduced recently. The performance of the ABC algorithm has been proved to be very effective in many researches. In this paper, ABC algorithm combined with kernel strategy is proposed for clustering semi-supervised information. The proposed clustering strategy can make use of more background knowledge than traditional clustering methods and deal with non-square clusters with arbitrary shape. Several datasets including 2D display data and UCI datasets are used to test the performance of the proposed algorithm and the experiment results indicate that the constructed algorithm is effective.
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Acknowledgements
This work was partially supported by the National Natural Science Foundation of China (No. 61473259, No. 61070074, No. 60703038), the Zhejiang Provincial Natural Science Foundation (No. Y14F020118), the National Science& Technology Support Program of China (2015BAK26B00, 2015BAK26B02) and the PEIYANG Young Scholars Program of Tianjin University (2016XRX-0001).
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Dai, J., Han, H., Hu, H., Hu, Q., Wei, B., Yan, Y. (2016). Semi-supervised Clustering Based on Artificial Bee Colony Algorithm with Kernel Strategy. In: Cui, B., Zhang, N., Xu, J., Lian, X., Liu, D. (eds) Web-Age Information Management. WAIM 2016. Lecture Notes in Computer Science(), vol 9659. Springer, Cham. https://doi.org/10.1007/978-3-319-39958-4_32
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DOI: https://doi.org/10.1007/978-3-319-39958-4_32
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