文献类型：期刊文献

英文题名：An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning

作者：Wang, Heng[1];Zhao, Zhenzhen[2];Guo, Zhiwei[3];Wang, Zhenfeng[1];Xu, Guangyin[1]

第一作者：Wang, Heng

通讯作者：Xu, GY[1]

机构：[1]Henan Agr Univ, Collaborat Innovat Ctr Biomass Energy, Zhengzhou 450002, Henan, Peoples R China;[2]Henan Univ Econ & Law, Coll Comp & Informat Engn, Zhengzhou 450002, Henan, Peoples R China;[3]Chongqing Univ, Coll Commun Engn, Chongqing 400044, Peoples R China

第一机构：Henan Agr Univ, Collaborat Innovat Ctr Biomass Energy, Zhengzhou 450002, Henan, Peoples R China

通讯机构：[1]corresponding author), Henan Agr Univ, Collaborat Innovat Ctr Biomass Energy, Zhengzhou 450002, Henan, Peoples R China.

年份：2017

卷号：2017

外文期刊名：COMPLEXITY

收录：;Scopus(收录号：2-s2.0-85021874592);WOS:【SCI-EXPANDED(收录号:WOS:000404936100001)】；

基金：This work has been partly supported by Henan Provincial Department of Science and Technology Research Project (172102210307), Henan Province Institution of Higher Learning Youth Backbone Teachers Training Program (2016GGJS036), and Key Science Research Program of Henan Province (17A480004, 16A413010).

语种：英文

摘要：The occurrence of series of events is always associated with the news report, social network, and Internet media. In this paper, a detecting system for public security events is designed, which carries out clustering operation to cluster relevant text data, in order to benefit relevant departments by evaluation and handling. Firstly, texts are mapped into three-dimensional space using the vector space model. Then, to overcome the shortcoming of the traditional clustering algorithm, an improved fuzzy c-means (FCM) algorithm based on adaptive genetic algorithm and semisupervised learning is proposed. In the proposed algorithm, adaptive genetic algorithm is employed to select optimal initial clustering centers. Meanwhile, motivated by semisupervised learning, guiding effect of prior knowledge is used to accelerate iterative process. Finally, simulation experiments are conducted from two aspects of qualitative analysis and quantitative analysis, which demonstrate that the proposed algorithm performs excellently in improving clustering centers, clustering results, and consuming time.