A Study on the Characteristics of College Students’ Consumption Behavior Based on Clustering and Association Rules

2021 ◽  
pp. 370-380
Author(s):  
Jie Wang ◽  
XiWen Chen ◽  
KaiRui Cheng ◽  
YanLi Cao ◽  
Bin Pan
Keyword(s):  
College Students ◽  
Association Rules ◽  
Consumption Behavior ◽  
Behavior Based

scholarly journals Unsupervised Detection of Abnormal Electricity Consumption Behavior Based on Feature Engineering

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 55483-55500 ◽  
Author(s):  
Wei Zhang ◽  
Xiaowei Dong ◽  
Huaibao Li ◽  
Jin Xu ◽  
Dan Wang
Keyword(s):  
Electricity Consumption ◽  
Feature Engineering ◽  
Consumption Behavior ◽  
Behavior Based

scholarly journals Mathematical Modeling of the Spread of Alcoholism Among Colombian College Students

2020 ◽  
Vol 16 (32) ◽  
pp. 195-223
Author(s):  
Edgardo Pérez
Keyword(s):  
Mathematical Modeling ◽  
College Students ◽  
Mathematical Model ◽  
Drinking Behavior ◽  
Preventive Measure ◽  
Nonlinear Mathematical Model ◽  
Consumption Behavior ◽  
Existence And Stability ◽  
Drugs And Crime ◽  
The Impact

In this paper, we present a nonlinear mathematical model, describing the spread of high-risk alcohol consumption behavior among college students in Colombia. We proved the existence and stability of the alcohol-free and drinking state equilibrium by means of Lyapunov function and LaSalle’s invariance principle. Also, we apply optimal control to study the impact of a preventive measure on the spread of drinking behavior among college students. Finally, we use numerical simulations and available data provided by the United Nations Office on Drugs and Crime (UNODC) and the Colombian Ministry of Justice to validate the obtained mathematical model.

scholarly journals Analysis of Excess Consumption in College Students by KPAG Method During the COVID-19 Pandemic

2021 ◽  
Vol 2 (1) ◽  
pp. 1-3
Author(s):  
Bin Zhao ◽  
◽  
Jinming Cao ◽  
Keyword(s):  
College Students ◽  
Early Warning ◽  
Principal Component ◽  
Real Data ◽  
Kernel Principal Component Analysis ◽  
Polynomial Kernel ◽  
Consumption Behavior ◽  
Fitting Method ◽  
Data Dimensionality Reduction ◽  
Excessive Consumption

With the arrival of COVID-19, some areas are under closed management, bringing about changes in the way people consume. It also leads to the excessive consumption of some people, especially college students. In order to give early warning to unreasonable consumption behavior, this study designed KPAG algorithm to give early warning to consumption risk. Using particle swarm optimization (PSO) kernel principal component analysis (KPCA) parameter optimization, optimal polynomial kernel to delete data information, and ant colony genetic algorithm (association) clustering analysis of data dimensionality reduction, according to the consumption behavior of college students are divided into three categories, for the consumption behavior of college students to build an early warning model. Through the classification and verification experiment of real data, the results show that compared with the traditional PCA data fitting method, the accuracy of the model in this paper can reach 90%, which is more reliable than the traditional algorithm, and the accuracy of the model is improved by nearly 20%, which can be used for effective early warning.

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