An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

Jian, Li; Chen, Wan-ming

doi:10.1155/2020/6761597

Cited by 3 publications

(3 citation statements)

References 13 publications

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“…Grey clustering mainly uses the possibility function method to classify the evaluation index or the evaluation object according to the actual needs. Due to the unique advantages of grey clustering method comparatively to other methods in dealing with the clustering problem of "small sample, poor information" it has become an important research topic Novel evaluation approach (Li and Chen, 2020). Small samples are sufficient to demonstrate the feasibility of the model.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel evaluation approach for the ability of older adults based on grey clustering model

Zhang,

Liu,

et al. 2023

JM2

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Purpose The purpose of this study is to advance a novel evaluation index system and evaluation approach for ability of older adults in China. Design/methodology/approach This study constructed a comprehensive older adult ability evaluation index system with 4 primary indicators and 17 secondary indicators. Grey clustering analysis and entropy weight method are combined into a robust evaluation model for the ability of older adults. Findings The result demonstrates that the proposed grey clustering model is readily available to calculate the disability level of elderly individuals. The constructed index system more comprehensively considers all aspects of the disability of the elderly. Originality/value This study provides a quantitative method and a more reasonable index system for the determination of the disability level of the elderly.

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Section: Literature Reviewmentioning

confidence: 99%

A novel evaluation approach for the ability of older adults based on grey clustering model

Zhang,

Liu,

et al. 2023

JM2

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“…The grey clustering method has become an important research topic because of its unique advantages in dealing with the clustering problem of a small sample, poor information (Li and Chen, 2020; Guan et al ., 2020). Grey clustering is a method based on matrices of grey incidences to classify observation indices or observational objects into definable classes (Liu et al ., 2017a).…”

Section: Literature Reviewmentioning

confidence: 99%

Developing a risk assessment model for banking software development projects based on rough-grey set theory

Karimi

Yahyazade

2021

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PurposeRisk management is one of the most influential parts of project management that has a major impact on the success or failure of projects. Due to the increasing use of information technology in all fields and the high failure rate of software development projects, it is essential to predict the risk level of each project effectively before starting. Therefore, the main purpose of this paper is proposing an expert system to infer about the risk of new banking software development project.Design/methodology/approachIn this research, the risk of software developing projects is considered from four dimensions including risk of cost deviation, time deviation, quality deviation and scope deviation, which is examined by rough set theory (RST). The most important variables affecting the cost, time, quality and scope of projects are identified as condition attributes and four initial decision systems are constructed. Grey system theory is used to cluster the condition attributes and after data discretizing, eight rule models for each dimension of risk as a decision attribute are extracted using RST. The most validated model for each decision attribute is selected as an inference engine of the expert system, and finally a simple user interface is designed in order to predict the risk level of any new project by inserting the data of project attributesFindingsIn this paper, a high accuracy expert system is designed based on the combination of the grey clustering method and rough set modeling to predict the risks of each project before starting. Cross-validation of different rule models shows that the best model for determining cost deviation is Manual/Jonson/ORR model, and the most validated models for predicting the risk of time, quality and scope of projects are Entropy/Genetic/ORR, Manual/Genetic/FOR and Entropy/Genetic/ORR models; all of which are more than 90% accurateResearch limitations/implicationsIt is essential to gather data of previous cases to design a validated expert system. Since data documentation in the field of software development projects is not complete enough, grey set theory (GST) and RST are combined to improve the validity of the rule model. The proposed expert system can be used for risk assessment of new banking software projectsOriginality/valueThe risk assessment of software developing projects based on RST is a new approach in the field of risk management. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

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“…Since Professor Deng found the grey system theory, the grey clustering method has become an important research topic because of its unique advantages in dealing with the clustering problem of a small sample, poor information (Li and Chen, 2020). Grey clustering is a method based on matrices of grey incidences or whitenization weight functions of grey numbers to classify observation indices or observational objects into definable classes (Liu and Yang, 2017).…”

Section: Rough and Grey Set Theorymentioning

confidence: 99%

Corporate sustainability assessment based on rough-grey set theory

Karimi

Hojati

2021

JM2

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Purpose The purpose of this paper is to design an inference engine to measure the level of readiness of each bank before starting the corporate sustainability auditing process. Based on the output of the designed inference engine, the audition team can decide about the audition resources and the auditing process. Design/methodology/approach In this paper, the hybrid rough and grey set theory are used to design and create a rule model system to measure the sustainability level of banks. First, 16 rule models are extracted using rough set theory (RST), and the cross-validation of each model is done. Then, the grey clustering is used to combine the same condition attributes and improve the validity of the final model. A total of 16 new rule models are extracted based on the decreased condition attributes, and the best model is selected based on the cross-validation results. Findings By comparing the accuracy of rough-gray’s rule models and as a result of decreasing the condition attributes, a proper increase in the accuracy of all models is obtained. Finally, the Naive/Genetic/object-related reducts model with 95.6% accuracy is selected as an inference engine to measure new banks’ readiness level. Originality/value Sustainability measurement of banks based on RST is a new approach in the field of corporate sustainability. Furthermore, using the grey clustering for combining the condition attributes is a novel solution for improving the accuracy of the rule models.

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An Improved Grey Clustering Model with Multiattribute Spatial-Temporal Feature for Panel Data and Its Application

Cited by 3 publications

References 13 publications

A novel evaluation approach for the ability of older adults based on grey clustering model

A novel evaluation approach for the ability of older adults based on grey clustering model

Developing a risk assessment model for banking software development projects based on rough-grey set theory

Corporate sustainability assessment based on rough-grey set theory

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