Gray clustering model based on the degree of dynamic weighted incidence for panel data and its application

Hong-hua, WU; Qu, Zhongfeng

doi:10.1108/gs-09-2019-0040

Cited by 7 publications

(10 citation statements)

References 5 publications

(3 reference statements)

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“…e main problem to be solved is to analyse the characteristics of fans' aggressive behaviour from the psychological point of view and try to use advanced machine learning technology to dig out what factors induce fans' aggressive behaviour from a large number of psychological questionnaire data, to carry out effective prevention. Clustering is an important method in machine learning [13][14][15] and an important element in data mining, which is widely used in many fields, including intelligent business, image pattern recognition, statistics and pattern analysis, and information retrieval. e K-means clustering algorithm [16] is widely used in various fields for its simplicity, ease of implementation, efficiency, and scalability.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Heng-chuan

Liu

Zhen

2022

Security and Communication Networks

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Football is one of the most popular sports in the world. As the popularity of football continues to grow worldwide, so does the number of incidents of violence on the pitch. Today, doping, match fixing, black whistles, and football hooliganism are ranked as the four most toxic aspects of sport. How to study the factors that cause aggressive behaviour of fans from a psychological perspective has become a key issue in the field of sports. Therefore, this study proposes a method for mining the psychological factors of sport fan community members based on machine learning clustering. Firstly, three different members of a large fan community, i.e., university students, office workers, and unemployed people, are used as research subjects to investigate the psychological factors influencing fans’ aggressive behaviour using a questionnaire method. Secondly, the data obtained were mined and analysed using the K-means clustering algorithm in machine learning techniques. At the same time, a K-means initial clustering centre optimization algorithm based on principal component analysis (PCA) was proposed for the data characteristics of the interaction of psychological factors. The results show that the new algorithm significantly improves the quality of clustering compared with other optimization algorithms and accurately identifies the multiple factors that contribute to the occurrence of fan attacks.

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Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Heng-chuan

Liu

Zhen

2022

Security and Communication Networks

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“…At the same time, it is also a method that combines quantitative analysis and qualitative analysis, which improves the accuracy and completeness of the evaluation process to a certain extent. Gray whitening weight function clustering is mainly used to analyze whether the evaluation object belongs to different categories set in advance, so as to obtain the ability level of the evaluation object [17]. e advantage of gray whitening weight function cluster analysis method is that it has a small demand for sample data in the analysis process and does not have strict requirements for the statistical distribution of data [18].…”

Section: Construction Of the Port Capability Evaluation Modelmentioning

confidence: 99%

“…Gray range 1 (poor) Gray range 2 (middle) Gray range 3 (good) Gray range 4 (excellent) C1 f (-,-,5,9) f (5,9,-,13) f (9,13,-,17) f (13,17 Data Availability e data used to support the findings of this study are included within the article.…”

Section: Indexmentioning

confidence: 99%

Port Capability Evaluation from the Perspective of Supply Chain

Zhang

Qu²,

Dai

2021

Discrete Dynamics in Nature and Society

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Today’s ports have become an important node in the global supply chain. It is particularly important to make a scientific assessment of the comprehensive capabilities of the port and to provide a reference for the long-term development of the port. From the perspective of the supply chain, this article first selects the evaluation indicators that affect the port’s capabilities from four aspects of port resource ownership, control management, comprehensive services, and innovation-driven aspect. Secondly, we use the expert scoring method to judge the importance of the evaluation indicators and build a scientific and independent port capacity evaluation system from the perspective of the supply chain. Then, this paper uses the analytic hierarchy process to determine the weight coefficients of each evaluation index and uses the gray cluster analysis method of the triangular whitening weight function based on the center point to establish a qualitative and quantitative port capacity evaluation model from the perspective of the supply chain. Finally, we take a port in Northeast Asia as an example to conduct an empirical analysis to verify the feasibility of the port capacity evaluation system and model from the perspective of the supply chain constructed in this paper. The research results of this article can well analyze the port’s resource ownership, control and management capabilities, comprehensive service capabilities, and innovation-driven capabilities and provide a practical and effective theoretical basis for the port’s key development directions.

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“…It can divide the observable indexes and observation objects into multiple levels according to the grey incidence matrix or grey number whitening weight function (Han et al ., 2017). Common methods include grey incidence clustering (Wu and Qu, 2020) Professor Deng Julong's grey variable weight clustering method, and Professor Liu Sifeng's subsequent weighted grey clustering method and the grey clustering method based on triangle whitening weight function (Zeng et al ., 2022), etc. Later, Xiong et al .…”

Section: Introductionmentioning

confidence: 99%

Industrial ecological evaluation based on DPSIR and panel grey clustering model

Huang

Mengling

2023

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PurposeThis paper aims to build an improved grey panel clustering evaluation model and evaluate the comprehensive development potential of industrial economy, society and ecological environment in the Yangtze River Economic Belt of China. The purpose of this study is to provide some theoretical basis and tool support for management departments and relevant researchers engaged in industrial sustainable development.Design/methodology/approachThis study uses the driving force pressure state impact response analysis framework to build a comprehensive evaluation index system. Based on the center point triangle whitening weight function, it classifies the panel grey clustering of improvement time and index weight.FindingsThe results show that there are great differences in the level of industrial ecological development in different regions of the Yangtze River Economic Belt, which further illustrates the scientificity and rationality of the evaluation method proposed in this paper.Practical implicationsDue to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. The improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.Social implicationsDue to the industrial ecological development is in a constantly changing state, and the information is uncertain. Whitening weight function is introduced to represent the complete information of relevant data. The industrial ecological evaluation involves a comprehensive complex system, which belongs to the panel data analysis problem. In order to improve the effectiveness of industrial ecological evaluation, the improved grey panel clustering evaluation model is applied to grade the industrial ecological development level of the Yangtze River Economic Belt. The results have important guiding significance for the balanced development of industrial ecology in the region.Originality/valuethe new model proposed in this paper complements and improves the grey clustering analysis theory of panel data, that is, aiming at the subjective limitation of using time degree to determine time weight in panel grey clustering, a comprehensive theoretical method for determining time weight is creatively proposed. Combining the DPSIR (Driving force-Pressure-State-Influence-Response) model model with ecological development, a comprehensive evaluation model is constructed to make the evaluation results more authentic and comprehensive.

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Gray clustering model based on the degree of dynamic weighted incidence for panel data and its application

Cited by 7 publications

References 5 publications

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Machine Learning-Based Emotion Factor Analysis of Sport Fan Community

Port Capability Evaluation from the Perspective of Supply Chain

Industrial ecological evaluation based on DPSIR and panel grey clustering model

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