@Article{jqc.2020.09717,
AUTHOR = {Guorong Cui, Hao Li, Yachuan Zhang, Rongjing Bu, Yan Kang, Jinyuan Li, Yang Hu},
TITLE = {Weighted Particle Swarm Clustering Algorithm for Self-Organizing Maps},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {85--95},
URL = {http://www.techscience.com/jqc/v2n2/40346},
ISSN = {2579-0145},
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 of the WPSO algorithm is determined by the 
traditional clustering center is improved to the sample weight, and the cluster 
center is the “food” of the particle group. Each particle moves toward the nearest 
cluster center. Each iteration optimizes the particle position and velocity and uses 
K-means and K-medoids recalculates cluster centers and cluster partitions until 
the end of the algorithm convergence iteration. After a lot of experimental 
analysis on the commonly used UCI data set, this paper not only solves the 
shortcomings of K-means clustering algorithm, the problem of dependence of the 
initial clustering center, and improves the accuracy of clustering, but also avoids 
falling into the local optimum. The algorithm has good global convergence.},
DOI = {10.32604/jqc.2020.09717}
}