An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning
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 b...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2017/8130961 |
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| _version_ | 1832562759710015488 |
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| author | Heng Wang Zhenzhen Zhao Zhiwei Guo Zhenfeng Wang Guangyin Xu |
| author_facet | Heng Wang Zhenzhen Zhao Zhiwei Guo Zhenfeng Wang Guangyin Xu |
| author_sort | Heng Wang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-aec9875ef3694795ac4e1104556cb4db |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-aec9875ef3694795ac4e1104556cb4db2025-02-03T01:21:55ZengWileyComplexity1076-27871099-05262017-01-01201710.1155/2017/81309618130961An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised LearningHeng Wang0Zhenzhen Zhao1Zhiwei Guo2Zhenfeng Wang3Guangyin Xu4Collaborative Innovation Center of Biomass Energy, Henan Agricultural University, Henan 450002, ChinaCollege of Computer and Information Engineering, Henan University of Economics and Law, Henan 450002, ChinaCollege of Communication Engineering, Chongqing University, Chongqing 400044, ChinaCollaborative Innovation Center of Biomass Energy, Henan Agricultural University, Henan 450002, ChinaCollaborative Innovation Center of Biomass Energy, Henan Agricultural University, Henan 450002, ChinaThe 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.http://dx.doi.org/10.1155/2017/8130961 |
| spellingShingle | Heng Wang Zhenzhen Zhao Zhiwei Guo Zhenfeng Wang Guangyin Xu An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning Complexity |
| title | An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning |
| title_full | An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning |
| title_fullStr | An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning |
| title_full_unstemmed | An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning |
| title_short | An Improved Clustering Method for Detection System of Public Security Events Based on Genetic Algorithm and Semisupervised Learning |
| title_sort | improved clustering method for detection system of public security events based on genetic algorithm and semisupervised learning |
| url | http://dx.doi.org/10.1155/2017/8130961 |
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