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

Wang, Heng; Zhao, Zhenzhen; Guo, Zhiwei; Wang, Zhenfeng; Xu, Guangyin

doi:10.1155/2017/8130961

Cited by 7 publications

(5 citation statements)

References 27 publications

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“…Clustering consists of breaking down a set of elements down into smaller, more manageable groups, according to one or several parameters [18]. Clustering methods may use algorithms to attempt finding an optimum [19,20], or heuristics for proposing good enough solutions [21][22][23][24], like, for instance, genetic algorithms [25][26][27][28]. Clusters can be proposed using top-down or bottom-up (respectively, descendant or ascendant) approach, which is our choice in this work.…”

Section: Related Workmentioning

confidence: 99%

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Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

2019

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The way project actors are organized is crucial in determining how they will be able to collectively cope with nontrivial complex problems and risks. Current project organizations are generally based on single-criterion decomposition, whether product, process, or organization based. The proposed approach forms complementary clusters of actors based on the interdependencies between the risks they manage. More precisely, distinction has been made between the interdependencies connecting two risks that are owned by different actors and those owned by the same actor. We argue that interdependency between two risks managed by the same actor is less dangerous, meaning that clustering algorithm is tailored to distinguish mono- and biactor risk interdependencies. The complementary structure offered by interdependency-based clustering tends to put together strongly interconnected actors, albeit they were often initially not grouped together. It increases the likelihood of a better communication, coordination, and collective decision-making in complex situations. Some risks remain out of proposed clusters and are declared transverse, which means that their owners act as information hubs and are not involved in a single cluster. An industrial application is presented with operational results and perspectives for further work are drawn from it.

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Section: Related Workmentioning

confidence: 99%

“…(iii) They can be based on crisp or uncertain values, using for instance fuzzy clustering [28,[69][70][71][72] and spectral clustering [28,30,[73][74][75].…”

Section: Related Workmentioning

confidence: 99%

Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

2019

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“…This is exactly the aim of a cluster analysis, which is one of the standard approaches of data analysis in unsupervised machine learning techniques, in which data that have similarities are grouped in the same cluster. It is worth stating that, in related works, it is possible to find studies using clustering approaches, such as partitioned, hierarchical and density-based approaches (WANG et al, 2017). however, regarding applications in the area of public security, a gap can be observed, as studies tend to be more focused on the search for determinants of crimes and not on grouping regions according to similar crimes (LIMA & MARINHO, 2017).…”

Section: Introductionmentioning

confidence: 99%

A Clustering-Based Approach for Identifying Groups of Municipalities to Support the Direction of Public Security Policies

Costa

Silva

2022

Pesqui. Oper.

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The direction of public policies plays an important role in society as a whole, especially in security, which, in addition to being considered a necessity for every citizen, is constitutionally guaranteed. This study presents the use of an unsupervised learning approach for the establishment of clusters among the municipalities in the State of Pernambuco, Brazil, considering some types of representative crimes, aiming to direct actions to prevent and fight crime in order to support policy makers. The k-means algorithm was used as the main tool in the study, using the software R 3.6.1, and recommendations for actions were directed to each of the obtained clusters. To demonstrate the direction, the grouping with the parameter k = 26 was used, referring to the State Security Integration Areas. The results show that the use of a clustering approach for the municipalities provides greater effectiveness in directing actions to combat and prevent crime, given that the municipalities that have the greatest similarities are grouped in the same cluster.

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“…There are two main methods for event detection: unsupervised and supervised. Most unsupervised event detection is based on the clustered-base method [13][14][15]. Supervised event detection focuses on methods based on text features and methods based on neural networks [11].…”

Section: Introductionmentioning

confidence: 99%

Adapting the influences of publishers to perform news event detection

Chen

Wang

2021

Journal of Information Science

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Online news outlets have the power to influence public policy issues. To understand the opinions of the people, many government departments check online news outlets to manually detect events that interest people. This process is time-consuming. To promptly respond to public expectations, this research proposes a framework for detecting news events that may interest government departments. This article proposes a method for finding event trigger words used to represent an event. The news media can be a critical participant in ‘agenda-setting’, which means that more widely discussed news is more attractive and critical than news that is less discussed. However, few studies have considered the influence of news media publishers from the ‘agenda setting’ perspective. Therefore, this study proposes an ‘agenda setting’-based filter to establish a high-impact news event detection model. The proposed framework identifies trigger words and utilises word embedding to find news event–related words. After that, an event detection model is designed to determine the events that are attractive to government departments. The experimental results show that purity increases from 0.666 when no extraction method is used to 0.809 when the extraction method in this study is used. The overall improvement trend shows significant improvement in event detection performance.

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

Cited by 7 publications

References 27 publications

Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

Organizing Project Actors for Collective Decision‐Making about Interdependent Risks

A Clustering-Based Approach for Identifying Groups of Municipalities to Support the Direction of Public Security Policies

Adapting the influences of publishers to perform news event detection

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