Grey clustering model based on kernel and information field

2019 ◽  
Vol 10 (1) ◽  
pp. 56-67 ◽  
Author(s):  
Dang Luo ◽  
Zhang Huihui
Keyword(s):  
Weight Function ◽  
Fuzzy Clustering ◽  
Theoretical Basis ◽  
Disaster Risk ◽  
Content Type ◽  
Clustering Model ◽  
Grey Clustering ◽  
Drought Disaster ◽  
Regional Drought ◽  
Grey Number

Purpose 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. Design/methodology/approach 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. Findings 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. Originality/value 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.

Grey clustering evaluation based on AHP and interval grey number

2019 ◽  
Vol 12 (1) ◽  
pp. 127-137 ◽  
Author(s):  
Kejia Chen ◽  
Ping Chen ◽  
Lixi Yang ◽  
Lian Jin
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Analytic Hierarchy ◽  
Content Type ◽  
Evaluation Indexes ◽  
Clustering Model ◽  
Grey Clustering ◽  
Evaluation Problem ◽  
Grey Number ◽  
Mean Function

PurposeThe purpose of this paper is to propose a grey clustering evaluation model based on analytic hierarchy process (AHP) and interval grey number (IGN) to solve the clustering evaluation problem with IGNs.Design/methodology/approachFirst, the centre-point triangular whitenisation weight function with real numbers is built, and then by using interval mean function, the whitenisation weight function is extended to IGNs. The weights of evaluation indexes are determined by AHP. Finally, this model is used to evaluate the flight safety of a Chinese airline. The results indicate that the model is effective and reasonable.FindingsWhen IGN meets certain conditions, the centre-point triangular whitenisation weight function based on IGN is not multiple-cross and it is normative. It provides a certain standard and basis for obtaining the effective evaluation indexes and determining the scientific evaluation of the grey class.Originality/valueThe traditional grey clustering model is extended to the field of IGN. It can make full use of all the information of the IGN, so the result of the evaluation is more objective and reasonable, which provides supports for solving practical problems.

Drought grade assessment method based on grey cloud incidence clustering model

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Dang Luo ◽  
Yan Hu ◽  
Decai Sun
Keyword(s):  
Decision Making ◽  
Cloud Model ◽  
Henan Province ◽  
Maximum Deviation ◽  
Content Type ◽  
Risk Levels ◽  
Clustering Model ◽  
Subjective Weight ◽  
Drought Disaster ◽  
Grey Number

PurposeThe 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.Design/methodology/approachThe 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.FindingsThe 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.Practical implicationsIt provides an effective method for the assessment of drought disaster grade and the basis for formulating disaster prevention and mitigation plan.Originality/valueBy 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.

Quantitative analysis of the cities’ innovation capacity based on grey variable weight clustering

2015 ◽  
Vol 5 (3) ◽  
pp. 344-353
Author(s):  
Yeqing Guan ◽  
Hua Liu ◽  
Ying Zhu
Keyword(s):  
Quantitative Analysis ◽  
Weight Function ◽  
Design Methodology ◽  
Clustering Method ◽  
Content Type ◽  
Clustering Model ◽  
Variable Weight ◽  
Grey Clustering ◽  
Innovation Capacity

Purpose – The purpose of this paper is to find the reason which the results of grey variable weight clustering method do not correspond with the reality. It proposes reconstructing the whitenization weight function, outlining why and how inconsistency is avoided. The study aims to improve the model of grey clustering method based on the whitenization weight function and list the steps of the new clustering model so that analysis and application of innovation capacity in a broader range is normally found. Design/methodology/approach – First the reason for the problem that the clustering results of grey variable weight clustering do not correspond with the reality is analyzed in two existing literature. And then a new whitenization weight function is reconstructed, two properties of the whitenization weight function are proved. The solution of the new grey variable weight clustering based on the whitenization weight function is built by following six steps. Findings – The paper provides a new whitenization weight function which satisfies the normative and non-triplecrossing. It suggests that successful clustering results of innovation capacity act on two levels: integrating the elements of innovation capacity indexes, and following steps of grey variable weight clustering. Originality/value – This paper improves the existing method of grey variable weight clustering and fulfills an identified need to study how cities’ innovation capacity can be clustered.

A novel grey fixed weight cluster model based on interval grey numbers

2017 ◽  
Vol 7 (2) ◽  
pp. 156-167 ◽  
Author(s):  
Jing Ye ◽  
Yaoguo Dang
Keyword(s):  
Weight Function ◽  
Cluster Model ◽  
Medical Science ◽  
Grey System Theory ◽  
Central Point ◽  
Content Type ◽  
Model Based ◽  
Clustering Model ◽  
Fixed Weight ◽  
Grey Clustering

Purpose Nowadays, evaluation objects are becoming more and more complicated. The interval grey numbers can be used to more accurately express the evaluation objects. However, the information distribution of interval grey numbers is not balanced. The purpose of this paper is to introduce the central-point triangular whitenization weight function to solve the clustering process of this kind of numbers. Design/methodology/approach A new expression of the central-point triangular whitenization weight function is presented in this paper, in terms of the grey cluster problem based on interval grey numbers. By establishing the integral mean value function on the set of interval grey numbers, the application range of grey clustering model is extended to the interval grey number category, and, in this way, the grey fixed weight cluster model based on interval grey numbers is obtained. Findings The model is verified by a case which reveals a high distinguishability, validity and practicability. Practical implications This model can be used in many fields, such as agriculture, economy, geology and medical science, and provides a feasible method for evaluation schemes in performance evaluation, scheme selection, risk evaluation and so on. Originality/value The central-point triangular whitenization weight function is introduced. The method reflects the thought “make full use of the information” in grey system theory and further enriches the system of grey clustering theory as well as expands the application scope of the grey clustering method.

Grey cluster evaluation models based on mixed triangular whitenization weight functions

2015 ◽  
Vol 5 (3) ◽  
pp. 410-418 ◽  
Author(s):  
Si-feng Liu ◽  
Yingjie Yang ◽  
Zhi-geng Fang ◽  
Naiming Xie
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Weight Functions ◽  
The Novel ◽  
Content Type ◽  
Clustering Model ◽  
Cluster Evaluation ◽  
Fixed Weight ◽  
Variable Weight ◽  
Evaluation Models

Purpose – 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. Design/methodology/approach – 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. Findings – 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. Practical implications – A practical evaluation and decision problem for some projects in a university has been studied using the new triangular whitenization weight function. Originality/value – 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.

Research on safety evaluation of civil aircraft based on the grey clustering model

2018 ◽  
Vol 8 (1) ◽  
pp. 110-120 ◽  
Author(s):  
Bentao Su ◽  
Naiming Xie
Keyword(s):  
Safety Assessment ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Evaluation Index System ◽  
Safety Level ◽  
Content Type ◽  
Aircraft Manufacturing ◽  
Clustering Model ◽  
Grey Clustering ◽  
Civil Aircraft

Purpose The purpose of this paper is to construct a grey clustering model based on the nonlinear whitenization weight function and to assess the safety of civil aircraft by using a quantitative method. Design/methodology/approach According to the running stage of civil aircraft safety assessment issues, first the civil aircraft safety evaluation index system is constructed by using a qualitative method. Taking the information duplication between indicators, the grey relational analysis method is used to filter the key indicators, then the grey clustering evaluation model of nonlinear whitening right function is built to evaluate the safety of civil aircraft and the algorithm steps of the evaluation model are given. Finally, the model is validated by collecting the parameters of nine different civil aircrafts at home and abroad. Findings The results show that the safety level of different types of aircraft is different due to the different index parameters, and to some extent, explain the rationality and scientificity of the method proposed in this paper to solve the problem. Practical implications This paper gives a complete set of security assessment methods, which can be used to evaluate the security of civil aircraft in the operational phase quantitatively, scientifically and reasonably. Furthermore, it can be extended to other complex system security or stability assessment issues. Originality/value It not only provides the supplement and perfection of the safety assessment method in the theoretical system to a certain extent, but also provides a theoretical guidance to solve the problem of civil aircraft system safety assessment of civil aircraft manufacturing enterprise all over the world. At the same time, the nonlinear grey clustering evaluation model constructed in this paper is an improvement of the traditional model, which is, to some extent, the improvement of the grey clustering evaluation theory.

Evaluation of the Attack Effect Based on Improved Grey Clustering Model

2018 ◽  
Vol 10 (1) ◽  
pp. 92-100 ◽  
Author(s):  
Chen Yue ◽  
Lu Tianliang ◽  
Cai Manchun ◽  
Li Jingying
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Denial Of Service ◽  
Network Attacks ◽  
Network Attack ◽  
Denial Of Service Attack ◽  
Clustering Model ◽  
Grey Clustering ◽  
Service Attack ◽  
Attack And Defense

There are a lot of uncertainties and incomplete information problems on network attack. It is of great value to access the effect of the attack in the current network attack and defense. This paper examines the characteristics of network attacks, there are problems with traditional clustering that index attribution is not clear and the cross of clustering interval. A two-stage grey synthetic clustering evaluation model based on center-point triangular whitenization weight function was proposed for the attack effect. The authors studied the feasibility of applying this model to the evaluation of network attack effect. Finally, an example is given, which showed the model could evaluate the effect of the denial-of-service attack precisely. It is also shown that the model is viable to evaluate the attack effect.

Grey clustering evaluation of urban low-carbon transport development based on triangular whitenization weight function

2015 ◽  
Vol 5 (3) ◽  
pp. 278-289 ◽  
Author(s):  
Jun Guo ◽  
Xi Zhao ◽  
Yimin Huang
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Low Carbon ◽  
Content Type ◽  
Carbon Policy ◽  
Five Dimensions ◽  
Center Point ◽  
Grey Clustering ◽  
Transport Management ◽  
Carbon Transport

Purpose – The purpose of this paper is to establish a grey clustering evaluation model based on center-point triangular whitenization weight function to evaluate the situation of urban low-carbon transport development (LTD). The study results intend to provide some theoretical basis and tool support for transport management departments and related researchers who are engaged in low-carbon transport (LT). Design/methodology/approach – The study uses analytical hierarchy process based on expert investigations to determine the weight of each criteria, classifies the grey clusters based on center-point triangular whitenization weight function, calculates the membership of each development criteria and ranks the development level of all dimensions. Findings – The research results of case city show that low-carbon technology is in “poor” level, transport facility is in “superior” level, low-carbon policy and environmental coordination is in “intermediate” level, transport management is in “good” level and the overall LTD level is in “intermediate” level. Practical implications – Reducing the carbon emissions of urban transport and achieving LT is the key to promote urban sustainable development, the scientific judgment of transport development situation is the premise of promoting LTD. Therefore, based on the practices of LT in China, the study systematically clarifies LTD from five dimensions of reflecting LTD. Originality/value – From the perspective of sustainable development, the evaluation index system of LTD is built with five dimensions consisting of low-carbon technology, low-carbon policy, transport facility, transport management and environmental coordination. Then assess the LTD by using the grey clustering evaluation model based on center-point triangular whitenization weight. This paper presents a new research idea for LTD evaluation.

Two-stage grey cloud clustering model for drought risk assessment

2019 ◽  
Vol 10 (1) ◽  
pp. 68-84 ◽  
Author(s):  
Dang Luo ◽  
Manman Zhang ◽  
Huihui Zhang
Keyword(s):  
Henan Province ◽  
Clustering Coefficient ◽  
Drought Risk ◽  
Two Stage ◽  
Content Type ◽  
Clustering Model ◽  
Kernel Clustering ◽  
Grey Clustering ◽  
Coefficient Vector ◽  
Two Stages

Purpose 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. Design/methodology/approach 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. Findings 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. Practical implications 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. Originality/value 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.

Grey clustering evaluation based on triangular whitenization weight function of enterprise's management innovation performance

2014 ◽  
Vol 4 (3) ◽  
pp. 436-446 ◽  
Author(s):  
Tianbo Li ◽  
Ershi Qi ◽  
Yimin Huang
Keyword(s):  
Weight Function ◽  
Evaluation Model ◽  
Performance Level ◽  
Production Performance ◽  
Performance Criteria ◽  
Management Innovation ◽  
Content Type ◽  
Five Dimensions ◽  
Center Point ◽  
Grey Clustering

Purpose – The purpose of this paper is to attempt to establish a grey clustering evaluation model of center-point triangular whitenization weight function to measure the performance of enterprise's management innovation (MI). The author intends to provide some theory basis and tool support for enterprise's managers and other researchers who are engaged in performance measuring. Design/methodology/approach – The study uses questionnaire survey and expert interviews to determine the index weight of enterprise's MI performance (MIP), classifies the grey clusters based on center-point triangular whitenization weight function, calculates the membership of performance criteria and ranks the performance level of all dimensions. Findings – The survey data of case company shows that production performance is in superior level, employee and society influence performance are in satisfied level, finance and market performance are in intermediate level, total MIP is in satisfied level. Practical implications – MI is the fundamental way to keep enterprise's core competitiveness and achieve its strategic objectives. Performance is an effective tool to measure the MI. Therefore, based on the practices of MI in China, the study systematically clarifies the performance level of MI from five dimensions. Originality/value – The evaluation index of enterprise's MIP is built with five dimensions which contain production, market, finance, employee and social influence. The grey clustering evaluation model based on triangular whitenization weight function is applied to assess the performance criteria. This paper presents a new research idea for enterprise's performance evaluation.

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