Open Access
ARTICLE
A Clustering Method Based on Brain Storm Optimization Algorithm
Tianyu Wang, Yu Xue, Yan Zhao, Yuxiang Wang*, Yan Zhang, Yuxiang He
Nanjing University of Information Science & Technology, Nanjing, 210044, China
* Corresponding Author: Yuxiang Wang. Email:
Journal of Information Hiding and Privacy Protection 2020, 2(3), 135-142. https://doi.org/10.32604/jihpp.2020.010362
Received 02 August 2020; Accepted 29 August 2020; Issue published 18 December 2020
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.
Keywords
Cite This Article
T. Wang, Y. Xue, Y. Zhao, Y. Wang, Y. Zhang
et al., "A clustering method based on brain storm optimization algorithm,"
Journal of Information Hiding and Privacy Protection, vol. 2, no.3, pp. 135–142, 2020. https://doi.org/10.32604/jihpp.2020.010362