@Article{jihpp.2020.010362,
AUTHOR = {Tianyu Wang, Yu Xue, Yan Zhao, Yuxiang Wang, Yan Zhang, Yuxiang He},
TITLE = {A Clustering Method Based on Brain Storm Optimization Algorithm},
JOURNAL = {Journal of Information Hiding and Privacy Protection},
VOLUME = {2},
YEAR = {2020},
NUMBER = {3},
PAGES = {135--142},
URL = {http://www.techscience.com/jihpp/v2n3/40852},
ISSN = {2637-4226},
ABSTRACT = {In the field of data mining and machine learning, clustering is a 
typical issue which has been widely studied by many researchers, and lots of 
effective algorithms have been proposed, including K-means, fuzzy c-means 
(FCM) and DBSCAN. However, the traditional clustering methods are easily 
trapped into local optimum. Thus, many evolutionary-based clustering methods 
have been investigated. Considering the effectiveness of brain storm 
optimization (BSO) in increasing the diversity while the diversity optimization is 
performed, in this paper, we propose a new clustering model based on BSO to 
use the global ability of BSO. In our experiment, we apply the novel binary 
model to solve the problem. During the period of processing data, BSO was 
mainly utilized for iteration. Also, in the process of K-means, we set the more 
appropriate parameters selected to match it greatly. Four datasets were used in 
our experiment. In our model, BSO was first introduced in solving the clustering 
problem. With the algorithm running on each dataset repeatedly, our 
experimental results have obtained good convergence and diversity. In addition, 
by comparing the results with other clustering models, the BSO clustering model 
also guarantees high accuracy. Therefore, from many aspects, the simulation 
results show that the model of this paper has good performance.},
DOI = {10.32604/jihpp.2020.010362}
}