Abstract
This paper proposes a novel dynamic clustering algorithm called DCBAIN, which based on the artificial immune network and immune optimization algorithm. The algorithm includes two phases, it begins by running artificial immune network to find a clustering feasible solution (CFS), then it employs antibody clone algorithm (ACA) to get the optimal cluster number and cluster centers on the CFS. Some experimental results show that new algorithm has satisfied convergent probability and convergent speed.
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Zhong, J., Wu, ZF., Wu, KG., Ou, L., Zhu, ZZ., Zhou, Y. (2005). A Two-Phase Clustering Algorithm Based on Artificial Immune Network. In: Wang, L., Chen, K., Ong, Y.S. (eds) Advances in Natural Computation. ICNC 2005. Lecture Notes in Computer Science, vol 3611. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11539117_114
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DOI: https://doi.org/10.1007/11539117_114
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28325-6
Online ISBN: 978-3-540-31858-3
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