Risks prevention and control of university online public opinion based on data clustering algorithm

Author(s):  
Yuxiang Zou
2019 ◽  
Vol 162 ◽  
pp. 15-18
Author(s):  
Mingyue Jiang ◽  
Guowei Gao ◽  
Yirui Deng ◽  
Chenglong Wang

2022 ◽  
Vol 2152 (1) ◽  
pp. 012017
Author(s):  
Keke Zou

Abstract With the development of society, the material living standard of our people has been significantly improved, but we sacrificed the environment in the course of development, which led to the current number of environmental problems in our country is particularly large, so that now we need to pick up the tone of protecting the environment, so now the overall tone of the country is to protect the environment, adhere to the green water green mountain is the basic strategy of Jinshan Yinshan, play a good pollution prevention and control of the three major battles, care for the environment, protect the environment. And in the environment water is the most important, it carries everything, the purpose of this paper is to study based on water quality monitoring and pollution prevention and control of dynamic detection technology. In order to conduct the experiment better, after consulting the literature on water quality monitoring and pollution prevention and control, and dynamic detection technology, we used a variety of algorithms to construct a corresponding dynamic detection technology system to monitor water quality and conduct real-time surveys of pollutants, and obtain relevant experimental data to complete the experiment. The experimental results show that the improved adaptive parameter DBSCAN clustering algorithm is better than the AdaBoost algorithm and the genetic algorithm, so we finally choose to build a dynamic detection technology system using the improved adaptive parameter DBSCAN clustering algorithm.


Author(s):  
Yuexia Zhang ◽  
Ziyang Chen

Studying community discovery algorithms for complex networks is necessary to determine the origin of opinions, analyze the mechanisms of public opinion transmission, and control the evolution of public opinion. The problem of the existing clustering algorithm of the central node having a low quality of community detection must also be solved. This study proposes a community detection method based on the two-layer dissimilarity of the central node (TDCN-CD). First, the algorithm selects the central node through the degree and distance of the node. Selecting nodes in the same community as the central node at the same time is avoided. Simultaneously, the algorithm proposes the dissimilarity index of nodes based on two layers, which can deeply explore the heterogeneity of nodes and achieve the effect of accurate community division. The results of using Karate and Dolphins datasets for simulation show that compared to the Girvan–Newman and Fast–Newman classical community partitioning algorithms, the TDCN-CD algorithm can effectively detect the community structure and more accurately divide the community.  


2005 ◽  
Vol 24 (4, Suppl) ◽  
pp. S106-S110 ◽  
Author(s):  
Kevin D. McCaul ◽  
Ellen Peters ◽  
Wendy Nelson ◽  
Michael Stefanek

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