Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang*, Jinyuan Li, Yang Hu
Journal of Quantum Computing, Vol.2, No.2, pp. 85-95, 2020, DOI:10.32604/jqc.2020.09717
Abstract The traditional K-means clustering algorithm is difficult to determine
the cluster number, which is sensitive to the initialization of the clustering center
and easy to fall into local optimum. This paper proposes a clustering algorithm
based on self-organizing mapping network and weight particle swarm
optimization SOM&WPSO (Self-Organization Map and Weight Particle Swarm
Optimization). Firstly, the algorithm takes the competitive learning mechanism
of a self-organizing mapping network to divide the data samples into coarse
clusters and obtain the clustering center. Then, the obtained clustering center is
used as the initialization parameter of the weight particle swarm optimization
algorithm. The particle position… More >
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