Search results

The possibility function-based grey clustering model has evolved into a complete approach for dealing with uncertainty evaluation problems. Existing models still have problems with the choice dilemma of the maximum criteria and instances when the possibility function may not accurately capture the data's randomness. This study aims to propose a multi-stage skewed grey cloud clustering model that blends grey and randomness to overcome these problems.

First, the skewed grey cloud possibility (SGCP) function is defined, and its digital characteristics demonstrate that a normal cloud is a particular instance of a skewed cloud. Second, the border of the decision paradox of the maximum criterion is established. Third, using the skewed grey cloud kernel weight (SGCKW) transformation as a tool, the multi-stage skewed grey cloud clustering coefficient (SGCCC) vector is calculated and research items are clustered according to this multi-stage SGCCC vector with overall features. Finally, the multi-stage skewed grey cloud clustering model's solution steps are then provided.

The results of applying the model to the assessment of college students' capacity for innovation and entrepreneurship revealed that, in comparison to the traditional grey clustering model and the two-stage grey cloud clustering evaluation model, the proposed model's clustering results have higher identification and stability, which partially resolves the decision paradox of the maximum criterion.

Compared with current models, the proposed model in this study can dynamically depict the clustering process through multi-stage clustering, ensuring the stability and integrity of the clustering results and advancing grey system theory.

The purpose of this paper is to present two novel grey cluster evaluation models to solve the difficulty in extending the bounds of each clustering index of grey cluster evaluation models.

In this paper, the triangular whitenization weight function corresponding to class 1 is changed to a whitenization weight function of its lower measures, and the triangular whitenization weight function corresponding to class s is changed to a whitenization weight function of its upper measures. The difficulty in extending the bound of each clustering indicator is solved with this improvement.

The findings of this paper are the novel grey cluster evaluation models based on mixed centre-point triangular whitenization weight functions and the novel grey cluster evaluation models based on mixed end-point triangular whitenization weight functions.

A practical evaluation and decision problem for some projects in a university has been studied using the new triangular whitenization weight function.

Particularly, compared with grey variable weight clustering model and grey fixed weight clustering model, the grey cluster evaluation model using whitenization weight function is more suitable to be used to solve the problem of poor information clustering evaluation. The grey cluster evaluation model using endpoint triangular whitenization weight functions is suitable for the situation that all grey boundary is clear, but the most likely points belonging to each grey class are unknown; the grey cluster evaluation model using centre-point triangular whitenization weight functions is suitable for those problems where it is easier to judge the most likely points belonging to each grey class, but the grey boundary is not clear.

The purpose of this paper is to establish a two-stage grey cloud clustering model to assess the drought risk level of 18 prefecture-level cities in Henan Province.

The clustering process is divided into two stages. In the first stage, grey cloud clustering coefficient vectors are obtained by grey cloud clustering. In the second stage, with the help of the weight kernel clustering function, the general representation of the weight vector group of kernel clustering is given. And a new coefficient vector of kernel clustering that integrates the support factors of the adjacent components was obtained in this stage. The entropy resolution coefficient of grey cloud clustering coefficient vector is set as the demarcation line of the two stages, and a two-stage grey cloud clustering model, which combines grey and randomness, is proposed.

This paper demonstrates that 18 cities in Henan Province are divided into five categories, which are in accordance with five drought hazard levels. And the rationality and validity of this model is illustrated by comparing with other methods.

This paper provides a practical and effective new method for drought risk assessment and, then, provides theoretical support for the government and production departments to master drought information and formulate disaster prevention and mitigation measures.

The model in this paper not only solves the problem that the result and the rule of individual subjective judgment are always inconsistent owing to not fully considering the randomness of the possibility function, but also solves the problem that it’s difficult to ascertain the attribution of decision objects, when several components of grey clustering coefficient vector tend to be balanced. It provides a new idea for the development of the grey clustering model. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

The purpose of this paper is to establish a two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem with three-parameter interval grey number to evaluation of agricultural drought resistance grade of 18 cities in Henan Province.

The clustering process is divided into two stages. In the first stage: Combining variance and time degree, the time weight optimization model is established. Applying the prospect theory, the index weight optimization model is established. Then, with the help of grey possibility function, the first stage of grey cloud clustering evaluation is carried out. In the second stage: the weight vector group of kernel clustering is constructed, and the grey class of the object is determined. A two-stage grey cloud clustering model under the panel data for the multi-attribute clustering problem is proposed.

This paper indicates that 18 cities in Henan Province are divided into four categories. The drought capacity in Henan province is high in the east and low in the west, high in the south and low in the north and the central region is relatively stable. The drought is greatly affected by natural factors. And the rationality and validity of this model is illustrated by comparing with other methods.

This paper provides a practical method for drought resistance assessment, and provides theoretical support for farmers to grasp the drought information timely and improve the drought resistance ability.

The model in this paper solves the situation of ambiguity and randomness to some extent with the help of grey cloud possibility function. Moreover, the time weight of time degree and variance are used to reduce the volatility and the degree of subjective empowerment. Considering the risk attitude of the decision makers, the prospect theory is applied to make the index weight more objective. The rationality and validity of the model are illustrated by taking 18 cities in Henan Province as examples.

The purpose of this paper is to propose a grey clustering model based on kernel and information field to deal with the situation in which both the observation values and the turning points of the whitenization weight function are interval grey numbers.

First, the “unreduced axiom of degree of greyness” was expanded to obtain the inference of “information field not-reducing”. Then, based on the theoretical basis of inference, the expression of whitenization weight function with interval grey number was provided. The grey clustering model and fuzzy clustering model were compared to analyse the relationship and difference between the two models. Finally, the paper model and the fuzzy clustering model were applied to the example analysis, and the interval grey number clustering model was established to analyse the influencing factors of regional drought disaster risk in Henan Province.

The example analysis results illustrate that although the two clustering methods have different theoretical basis, they are suitable for dealing with complex systems with uncertainty or grey characteristic, solving the problem of incomplete system information, which has certain feasibility and rationality. The clustering results of case study show that five influencing factors of regional drought disaster risk in Henan Province are divided into three classes, consistent with the actual situation, and they show the validity and practicability of the clustering model.

The paper proposes a new whitenization weight function with interval grey number that can transform interval grey number operations into real number operations. It not only simplifies the calculation steps, but it has a great significance for the “small data sets and poor information” grey system and has a universal applicability.

Competition in the banking sector is more complex than in the past, and survival has become more difficult than before. The purpose of this paper is to propose a grey methodology for evaluating, clustering and ranking the performance of bank branches with imprecise and uncertain data in order to determine the relative status of each branch.

In this study, the two-stage data envelopment analysis model with grey data is applied to assess the efficiency of bank branches in terms of operations. The result of grey two-stage data envelopment analysis model is a grey number as efficiency value of each branch. In the following, the branches are classified into three grey categories of performance by grey clustering method, and the complete grey ranking of branches are performed using “minimax regret-based approach” and “whitening value rating”.

The results show that after grey clustering of 22 branches based on grey efficiency value obtained from the grey two-stage DEA model, 6 branches are assigned to “excellent” class, 4 branches to “good” class and 12 branches to “poor” class. Moreover, the results of MRA and whitening value rating models are integrated, and a complete ranking of 22 branches are presented.

Grey clustering of branches based on grey efficiency value can facilitate planning and policy-making for branches so that there is no need to plan separately for each branch. The grey ranking helps the branches find their current position compared to other branches, and the results can be a dashboard to find the best practices for benchmarking.

Compared with traditional DEA methods which use deterministic data and consider decision-making units as black boxes, in this research, a grey two-stage DEA model is proposed to evaluate the efficiency of bank branches. Furthermore, grey clustering and grey ranking of efficiency values are used as a novel solution for improving the accuracy of grey two-stage DEA results.

In multi-criteria decision-making with interval grey number information, decision makers usually take a risk to rank or choose some very similar alternatives. Additionally, when evaluating only one alternative, decision makers can only obtain a specific value using traditional decision-making methods and may find it hard to cluster the alternatives to the “correct class” because these methods lack predetermined reference points. To overcome this problem, this paper aims to propose a two-stage grey decision-making method.

First, a new type of clustering method for interval grey numbers is designed by proposing a new possibility function for grey numbers. Based on this clustering method, a new grey clustering evaluation model for interval grey numbers is proposed by which decision makers can obtain the grade rating information of each alternative. Then, according to the grey clustering evaluation model, a new two-stage decision-making method is introduced to solve the problem that some alternatives are very similar by designing a grey comprehensive decision coefficient of alternatives.

The authors propose a new grey clustering evaluation model to deal with interval grey numbers. They design a new model to obtain the membership degree for the interval grey numbers and then propose a new grey clustering evaluation model, which can evaluate only one alternative by predefined grey classes. Then, by the grey comprehensive decision coefficient, a two-stage grey evaluation decision-making method is put forward to solve the problem that some alternatives are very close and hard to be distinguished.

A new grey clustering evaluation model is proposed, which can evaluate only one alternative by predefined grey classes. A two-stage grey evaluation decision-making method is given to solve the problem that some alternatives are very close and hard to be distinguished.

The purpose of this paper is to solve the dilemma in the process of major selection decision-making.

Firstly, the group of weight vector with kernel has been defined. Then, the weighted comprehensive clustering coefficient vector was calculated based on the group of weight vector with kernel. Under the action of weighted comprehensive clustering coefficient vector, the information including in other components around component k and supporting object i to be classified into the k-th category has been gathered to component k. At last, a novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector is put forward to solve the dilemma in grey clustering evaluation. Then the overall evaluation conclusion can be consistent with the clustering result according to the rule of maximum value.

A new way to solve the dilemma in the process of major selection decision-making has been found. People can obtain a consistent result with two-stage decision model at the case of dilemma. That is, the conclusion of the overall evaluation is consistent with the clustering result according to the rule of maximum value.

Several functional groups of weight vector with kernel have been put forward. The proposed model can solve the clustering dilemma effectively and produce consistent results. A practical application of decision problem to solve the dilemma in supplier evaluation and selection of a key component of large commercial aircraft C919 have been completed by the novel two-stage decision model.

The two-stage decision model, the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector were presented in this paper firstly. People can solve the dilemma in grey clustering evaluation effectively by the novel two-stage decision model based on the group of weight vector with kernel and the weighted comprehensive clustering coefficient vector.

The purpose of this paper is to establish a grey cloud incidence clustering model to assess the drought disaster degree under 15 indexes in 18 cities of Henan province.

The grey incidence degree between each index and ideal index is used to determine the index weight and combined with the subjective weight, the comprehensive weight is given; the traditional possibility function is transformed into grey cloud possibility function by using the principle of maximum deviation and maximum entropy, which fully reflects the coexistence of multiple decision-making conclusions and constructs the grey cloud incidence clustering model.

The drought disaster degree of Henan province is divided into four grades under the selected 15 indexes. The drought grades show obvious regional differences. The risk levels of the east and southwest are higher, and the risk levels of the north and southeast are lower. This result is consistent with the study of drought disaster grades in Henan province, which shows the practicability and usefulness of the model.

It provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.

By studying the method of multiattribute and multistage decision-making with interval grey number information. The objective weight model of index value is designed, and the subjective weight is given by experts. On the basis of the two, the comprehensive weight of subjective and objective combination is proposed, which effectively weakens the randomness of subjective weight and reasonably reflects the practicality of index decision-making. The time weight reflects the dynamic of the index. The traditional possibility function is transformed into the grey cloud possibility function, which effectively takes advantage of the grey cloud model in dealing with the coexistence of fuzzy information, grey information and random information. Thus, the conflict between the decision-making results and the objective reality is effectively solved. The interval grey number can make full use of the effective information and improve the accuracy of the actual information.

This paper aims to establish a novel three-way decisions-based grey incidence analysis clustering approach and exploit it to extract information and rules implied in panel data.

Because of taking on the spatiotemporal characteristics, panel data can well-describe and depict the systematic and dynamic of the decision objects. However, it is difficult for traditional panel data analysis methods to efficiently extract information and rules implied in panel data. To effectively deal with panel data clustering problem, according to the spatiotemporal characteristics of panel data, from the three dimensions of absolute amount level, increasing amount level and volatility level, the authors define the conception of the comprehensive distance between decision objects, and then construct a novel grey incidence analysis clustering approach for panel data and study its computing mechanism of threshold value by exploiting the thought and method of three-way decisions; finally, the authors take a case of the clustering problems on the regional high-tech industrialization in China to illustrate the validity and rationality of the proposed model.

The results show that the proposed model can objectively determine the threshold value of clustering and achieve the extraction of information and rules inherent in the data panel.

The novel model proposed in the paper can well-describe and resolve panel data clustering problem and efficiently extract information and rules implied in panel data.

The proposed model can deal with panel data clustering problem and realize the extraction of information and rules inherent in the data panel.