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

Autor: Heng Wang, Zhenzhen Zhao, Zhiwei Guo, Zhenfeng Wang, Guangyin Xu
Jazyk: angličtina
Rok vydání: 2017
Předmět:
Zdroj: Complexity, Vol 2017 (2017)
Druh dokumentu: article
ISSN: 1076-2787
1099-0526
DOI: 10.1155/2017/8130961
Popis: 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.
Databáze: Directory of Open Access Journals
Externí odkaz: