Abstract
In this paper, we propose an improved fuzzy C-means clustering algorithm based on cooperative quantum-behaved particle swarm optimization with learning behavior. Though FCM is a widely used clustering method, it has the inherent limitation of being sensitive to initial value and prone to fall in local optimum. To address this problem, we utilize the widely used global searching algorithm—QPSO, and employ new strategies to enhance its performance. First, we use the cooperative evolution strategy to improve the global searching capacity. Second, for each particle, the behavior of learning from others is granted, which effectively boosts the local searching capability. Furthermore, a gene pool is constructed to share information among all subgroups periodically. Since the iteration process is replaced by the improved version of QPSO, FCM no longer depends on the initialization values. Our experiments show that the proposed algorithm outperforms FCM and its improved versions significantly. The convergence and clustering accuracy are both improved effectively.
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Acknowledgments
This work is supported by National Natural Science Foundation of China (NSFC Grant No. 61272258, 61170124, 61301299, 61272005), and a prospective joint re-search projects from joint innovation and research foundation of Jiangsu Province (BY2014059-14).
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Lu, P., Dong, H., Zhai, H., Gong, S. (2015). Fuzzy C-means Based on Cooperative QPSO with Learning Behavior. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_34
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DOI: https://doi.org/10.1007/978-3-319-23862-3_34
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