Abstract
The traditional Fuzzy c-means (FCM) clustering algorithm is sensitive to initial seeds and noise. A novel fuzzy clustering algorithm is proposed by Peng Hong, which is named as Fuzzy-MC. However, due to the limitation of the serial architecture of current computer, the parallel and distributed computing characteristics of P system was not able to be exhibited. Therefore, Fuzzy-MC algorithm increases its computing time. To reduce the computing time, this paper proposes a GPU-based parallel Fuzzy-MC algorithm. In the parallel algorithm, block layer in CUDA programming model is used to represent the cells, while threads are used to realize the evolution and communication of the objects. Two artificial data sets and four real-life data sets from the UCI data sets are chosen to compare parallel version and the corresponding serial version. The computing time and clustering performance are used to demonstrate the effectiveness of the proposed algorithm. Compared with the Fuzzy-MC algorithm, parallel Fuzzy-MC algorithm not only ensures the clustering performance but also reduce its computing time.
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Acknowledgments
The authors first sincerely thank the editors and anonymous reviewers for their constructive comments and suggestions. The authors would also like to thank Prof. H Peng from XiHua University (China). This work is supported by the Key research base of philosophy and Social Sciences in Sichuan province and key research base of Humanities and Social Sciences in Sichuan colleges and Universities (No. SHZLQN1701).
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Zhang, C., Yue, Z., Jin, J., Yan, D. (2019). A Parallel Framework for Fuzzy Membrane Clustering Based on P Systems and Improved PSO. In: Deng, K., Yu, Z., Patnaik, S., Wang, J. (eds) Recent Developments in Mechatronics and Intelligent Robotics. ICMIR 2018. Advances in Intelligent Systems and Computing, vol 856. Springer, Cham. https://doi.org/10.1007/978-3-030-00214-5_138
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DOI: https://doi.org/10.1007/978-3-030-00214-5_138
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