Grey Clustering Method Research Articles

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

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.

Assessing the Impact of Advanced Technologies on Utilization Improvement of Substations

The smart substation is the heart of a transmission system, which is particularly emphasized as the most significant composition of smart grids in China. In order to assess the functionality performance of substation technologies, this paper presents methods used to identify the most promising solutions for smart substation design and to evaluate the technical levels of available technologies. The multi-index optimization model is presented to address the issue of smart substation planning. A mathematical model of the planning decision problem is established with multiple objectives consisting of economic, reliability, and green key indices, and many kinds of concerns including physical and environmentally friendly operations are formulated as a set of constraints. With respect to the assessment of the technical level regarding integration of advanced technologies into a substation, a modified grey whitenization weight function is adopted to structure a novel grey clustering method. The proposed grey clustering approach is used to overcome the difficulty of insufficient quantitative assessment capacity for traditional methods. The evaluation of technical effects provides the classification definition for the development phase and the maturity level of the smart substation. The effectiveness of the proposed approaches in planning decision-making and evaluation of construction efforts is demonstrated with case studies involving the actual smart substation projects of Wenchongkou substation in China Southern Power Grid (CSG) and Mengzi substation in State Grid Corporation of China (SGCC).

A Hierarchical Clustering Approach Based on Three-Dimensional Gray Relational Analysis for Clustering a Large Group of Decision Makers with Double Information

Two types of information, collectively referred to as double information, are usually required in management decision-making. The first is preference information expressed in a judgment matrix. The second is reference information expressed in a multi-attribute decision matrix. In this paper, we investigate large-scale group clustering problems with double information in group decision-making. We first establish a novel three-dimensional gray correlation degree index, which integrates the alternative decision-making vector, index vector and alternative preference vector, to fully excavate the correlation between decision makers with double information. We then develop a new clustering procedure combining three-dimensional gray relational analysis and the concept of hierarchical clustering. Moreover, a model for determining clustering centers is established on the basis of the maximum gray correlation degree within each cluster and minimum gray correlation degree among clusters. A heuristic algorithm for the model to identify the core decision maker in each cluster is proposed. Finally, we illustrate the applications of the developed procedures with a practical case. The rationality of the proposed method is demonstrated by comparing results with results obtained using other methods, including the traditional gray clustering method and hierarchical clustering method with single information; i.e., preference information or reference information.

An integrated framework for effective safety management evaluation: Application of an improved grey clustering measurement

Safety evaluation is an important and challenging issue in many industries and is also a key component of risk management. Various evaluation methods have been proposed to make safety evaluation more consistent and objective. However, a major concern is that many existing safety evaluation measurements are still subjective and are not easy to obtain in a uniform way. This paper aims to develop a framework suitable for evaluating the safety performance at organizational or project levels in a comprehensive way that may be expected to reflect all risk assessment aspects and make best use of professional talent and experiences from different evaluators. In this paper, a structural evaluation logic is proposed, based on an improved grey clustering method. First, a grey clustering-based indicator system is developed to avoid the arbitrary selection of indicators. Then, a novel interval-grey-number reciprocal-judgment-matrix based AHP (GRAHP) is proposed that extends the classical analytic hierarchy process to deal with the possible contradictory opinions from experts in different fields as well as the standardization problem of collected independent and uncertain data in evaluation. Additionally, the transformation and optimization method of the new proposed grey hierarchy analysis model are given. Finally, an improved grey variable weight clustering evaluation model is built based on the above described methods. We illustrate the practical implementation of the proposed methods using actual aviation data from China. The results show that the proposed framework and methods have good ability in safety evaluation for large and complex engineering projects.

基于熵权理论与灰色定权聚类法的企业电力能效评估模型研究与应用

本文通过采用熵权理论和灰色定权聚类法相结合的企业电力能效评估模型，对企业电力能效的评估方法进行研究与应用。文章先对电力能效评估模型建立指标权重的过程进行了系统分析，又通过灰色定权聚类法对能效综合评估模型的建模过程进行分析，从而建立了一种对企业电力能效进行系统评估的方法。该方法不仅科学地处理了指标权重在定量化过程中主观判断的问题，而且以企业聚类系数最大化为出发点，充分利用了各指标数据已有的客观信息及隐含的灰色特性，对指标进行综合治理，得到各个企业的电力能效状况，从而为企业的节能降耗提供很好的依据。本文最后还以三家企业为评估对象，对三家企业的电力能效进行了详细的建模分析，验证了该方法的可行性。 By using the enterprise power efficiency evaluation model based on entropy theory and grey fixed-weight clustering method, this article studies and applies a method of enterprise power efficiency evaluation. Firstly the author analyzes the process of how the index weight of power efficiency evaluation model is established, and then analyzes the process of modeling the comprehensive power efficiency evaluation based on grey fixed-weight clustering method. Accordingly, a method to systematically evaluate the enterprise power efficiency is established. This method not only has scientifically dealt with the problem—subjective judgment in the process of quantifying the index weight, but also makes full use of the index data of the existing objective information and the implicit grey characteristics to maximize the enterprise clustering coefficient. Meanwhile, this method can also comprehensively process the various indexes, and figure out the precise power efficiency of each enterprise to provide a good basis of saving energy and reducing consumption for the enterprise evaluated. Finally, this article, taking the evaluative object of three companies as basis, models and analyzes these three companies’ power efficiency, and verifies the feasibility of the method.

Condition Assessment for Power Transformer Based on Improved Evidence Combination Rule

In view of the uncertainty transformer state information, state of transformer hierarchical evaluation model is established. Firstly, index system of transformer state is formed; Secondly, in order to get comprehensive index of state, grey clustering method is used for monomial index clustering analysis; Finally in the basis of improving synthesis rules of conflict evidence of D - S evidence theory , clustering coefficient is used as the basic probability assignment function to evaluate the overall state of transformer. Example analysis results show that the proposed evaluation model can accurately grasp the running state of transformer and provide guidance for transformer condition overhaul.

The assessment of submarine slope instability in Baiyun Sag using gray clustering method

It is a significant task to evaluate the seafloor stability for the exploitation of marine resources and the construction of marine structures. This paper analyzes the impact factors of submarine slope instability using geological and geophysical data of the Baiyun Sag in the Pearl River Mouth Basin and builds the evaluation index system of submarine slope instability. The weight of each index is calculated by analytic hierarchy process method to weaken subjective effect. Gray clustering method is applied to estimate the risk of submarine slope instability in Baiyun Sag qualitatively and quantitatively. The assessment standard is divided into five grades, and the whitening function of each gray cluster is determined by the assessment scheme. The gray evaluation weight vector of each indicator is calculated by the table-dispatching method. The gray assessment value is estimated by making the gray assessment weight vector single value, and the gray grade is determined according to the maximum principle. The evaluation results show quantitatively that the risk grade of submarine slope instability in the Baiyun Sag is medium, and the gray clustering method is reasonable and feasible.

Assessment of Sediment Quality in Jiangsu Coastal Ocean Based on Grey Clustering Method

Thirty-nine samples of surface sediments obtained from Jiangsu coastal ocean were analysed to evaluate the sediment quality. The grey clustering method is applied to assess the sediment quality and generate the classified results. Then the samples are classified into three categories (clean, light pollution and heavy pollution). Of all the thirty-nine samples, there are eleven clean samples, twenty light pollution samples, and eight heavy pollution samples. The results show that the pollution status has the regional feature of concentrating distribution. By analysing underlying reasons, pollutants discharging into the sea due to increased industrial and agricultural activities contributed to the contamination. Therefore, more emphasis should be paid on the surface tidal flat sediment environment administration, especially on the treatment of pollution source for improving the sediment quality. It is very important to enhance the marine environmental protection for sustainable development of coastal area.

A grey rough set model for evaluation and selection of software cost estimation methods

Purpose – Due to the limitation of acknowledgment, the complexity of software system and the interference of noises, this paper aims to solve the traditional problem: traditional software cost estimation methods face the challenge of poor and uncertain inputs. Design/methodology/approach – Under such circumstances, different cost estimation methods vary greatly on estimation accuracy and effectiveness. Therefore, it is crucial to perform evaluation and selection on estimation methods against a poor information database. This paper presents a grey rough set model by introducing grey system theory into rough set based analysis, aiming for a better choice of software cost estimation method on accuracy and effectiveness. Findings – The results are very encouraging in the sense of comparison among four machine learning techniques and thus indicate it an effective approach to evaluate software cost estimation method where insufficient information is provided. Practical implications – Based on the grey rough set model, the decision targets can be classified approximately. Furthermore, the grey of information and the limitation of cognition can be overcome during the use of the grey rough interval correlation cluster method. Originality/value – This paper proposed the grey rough set model combining grey system theory with rough set for software cost estimation method evaluation and selection.

Grouping Parts for Multiple Parts Production in Additive Manufacturing

Rapid prototyping (RP) has evolved to Additive Manufacturing (AM) in recent years. It can produce functional or end-use parts with small or even medium quantities. And further, due to its unique layer-by-layer construction principle, it can produce different parts at the same time in a same AM machine. To improve the productivity and machine utilization of AM processes under multiple parts production context, this paper propose the conception of ‘Grouping parts’. Based on the Group Technology (GT) used in traditional processing technologies, a modified Group Technology for AM under multiple parts manufacturing context is presented. To group parts, a set of key attributes affecting the AM production time, cost, quality and work preparation are identified to represent the parts, and then a Grey Clustering method is adopted to conduct the similarity analysis. A simple case study is presented in the end to illustrate the proposed conception and its methodology.

An Efficient Approach to Identify Salt Tolerance of Upland Cotton at Seedling Stage

Three-leaf cotton seedlings of two salt-tolerant varieties and two salt-sensitive varieties were treated by water and 4%(40 g L–1) NaCl solution, respectively. A total of 13 parameters related to salt tolerance including salt injury index(SII), shoot fresh weight(SRW), root fresh weight(RFW), leaf relative water content(RWC), chlorophyll fluorescence parameters(Fo, Fm, Fv/Fm), relative conductivity(RC), manlondialdehyde(MDA) content and the activity of antioxidant enzymes were monitored after 72 h of treatment. Comprehensive assessment of salinity tolerance based on grey relation clustering method, principal component analysis and stepwise regression analysis indicated that the maximum quantum yield of PSII was the most significant correlated indices with salt tolerance in upland cotton, which could be used as a single parameter to assess salt tolerance based on the equation: y = 1.943x – 0.882(where y is the salt tolerance index, x is the related value of maximum efficiency of photosystem II). The other two salt-tolerant varieties and two salt-sensitive varieties were used to rate the classification of salt tolerance. Salt tolerance index(y) of 23 varieties with known salt tolerance were calculated to validate the accuracy of the equation, the result was consistent with field investigation. In this study, the maximum efficiency of photosystem II was used as the main index to validate the salt tolerance in cotton, together with the construction of salt tolerance index equation and salt tolerance rating, which could greatly improve the efficiency of salinity tolerance evaluation for massive germplasm in future.

Evaluation Study of Grounding Grids Based on Gray Clustering

Grounding grid is an important device which ensures the safe operation of substation. Its corrosion is influenced by various factors and the influence of these factors is varied. It is difficult to infer the relationship between the corrosion rate and its influencing factors, so this paper apply the grey clustering method of grey system theory in the grounding grid corrosion and gives an application instance and realizes it by MATLAB. The results demonstrate the practicality and effectiveness of the method mentioned in this paper. This method can provide reference for high reliability for related departments in the implementation of emergency situation due to grounding grid corrosion.

Using improved Gray Clustering Method to evaluate the degree of damage to arable lands in mining areas

为更加客观准确地反映矿区耕地的损毁程度, 更好地服务于耕地保护与土地复垦, 针对经典灰色聚类法单纯用阈值法确定权重, 不能较好地反映指标的损毁程度对聚类对象影响的局限性, 本文设定了有效土层厚度为主导限制性指标, 并将相对限制性指数融入到灰色聚类法中, 构建了GCM_DR(Grey Clustering Method_Dominant Restrictive Indicator & Relatively Restrictive Index)矿区耕地损毁程度评价模型, 并选用我国某高潜水位矿区塌陷损毁耕地进行了评价模型应用的验证。结果表明: 研究区2个塌陷盆地共计8个评价单元的损毁等级分别为Ⅱ、Ⅲ、Ⅳ、Ⅴ、Ⅰ、Ⅱ、Ⅲ、Ⅲ, 该趋势与其他方法基本一致, 结合实地调研情况, 表现了较高的可靠性; 主导限制性指标的设定使得该模型既考虑到了综合因素又兼顾主导因素, 一定程度上减小了评价任务的工作量, 提高评价工作的效率; 与经典灰色聚类模型相比, 改进后的模型强调指标的相对限制性, 损毁程度大的指标权重较大, 聚类系数向损毁级别高的方向倾斜, 表现了较好的灵敏性; 在确定权重时改进后的模型强调聚类指标的相对损毁程度, 一定程度上减小了分级跨度不同导致的权重分配的不合理性。因此, GCM_DR模型可应用于矿区耕地损毁程度评价工作中, 在了解耕地损毁现状、编制土地复垦方案、确定赔偿责任范围等方面有较好的应用价值。

Research on Network Partitioning Based on Expanded Grey Cluster Theory

the voltage stability discriminant method of short circuit capacity indicators based on measured trajectory needs a great deal of computation time while dynamic Thevenin equivalent need to be done to all the nodes, it is unable to meet the requirements of rapid real-time sentenced to stability. On this foundation, this paper proposes a quick network partitioning method. Firstly, start the real-time disturbance identification criterion and pick up the bus nods triggered by the disturbance. Then split the bus nods into k subsets using Expanded Grey Cluster method. Finally evaluate stability by parallel computing. The method is proved simple, efficient and practical through simulation.

Safety Assessment of Subway Station Operation Based on Grey Clustering Method

The safety evaluation index of subway station operation is established from the passengers, equipments and facilities ability, emergency ability and other aspects. Specific to the limitation the traditional entropy weighting method, the improved vertical and horizontal weighting method is puts forward, and safety evaluation method of the subway station operation based on grey clustering method is proposed. Finally, taking the Beijing subway stations in China as an example, the rationality and reliability of this method is verified.

Mining stable patterns in multiple correlated databases

Many kinds of patterns (e.g., association rules, negative association rules, sequential patterns, and temporal patterns) have been studied for various applications, but very little work has been reported on multiple correlated databases that are all relevant. This paper proposes an efficient method for mining stable patterns from multiple correlated databases. First, we define the notion of stable items according to two constraint conditions, minsupp and varivalue. We then measure the similarity between stable items based on gray relational analysis, and present a hierarchical gray clustering method for mining stable patterns consisting of stable items. Finally, experiments are conducted on four datasets, and the results of the experiments show that our method is useful and efficient.

Research on Stage Index Construction and Fuzzy Comprehensive Evaluation on Enterprise Informatization

This study presents an application of the AHP in the performance evaluation of information system by developing a fuzzy comprehensive evaluation model and a grey clustering method. Its aim is to propose a dynamic analytical framework on enterprise informatization evaluation based on the stage characteristics of implementation. Firstly, it summarizes and compares the existing literatures on informatization evaluation. Secondly, it focuses on the stage characteristics of enterprise informatization’s implementation. Thirdly, it proposes an evaluation framework consisting of five elements and indices system aiming at three stages of enterprise informatization. Finally, a case study is illustrated the application of the evaluation model and the evaluation process.

Evaluation of Seawater Quality in Tianjin Offshore Based on Grey Clustering Method

The seawater quality in Tianjin offshore area from 2006a to 2007a was evaluated based on the Grey Clustering Method (GCM). The results showed that pollution was serious in the northern coast of the study area, but other areas had good water quality. After analyzing, we found that both the selected environmental factors and the factor numbers for evaluation affected result of grey clustering, especially when the environmental factors were of great difference in the pollution level. Further study is recommended to improve and perfect the grey clustering method in order to get more unified application, and it is also beneficial for the comparison of different results.

Assessment of Regional Geological Disaster on Grey Clustering Methed in Shulan

Gray clustering method was used to establish the regional geological disaster risk assessment model by factor of the mean annual rainfall, relative height, ground slope ，geomorphological type, rock and soil type, fracture density, earthquake intensity, vegetation cover rate, human activity. This model was used to Predictive assessment of geological disasters in the Jilin Shulan region which is divided into the Ⅰ, Ⅱ, Ⅲ, Ⅳ districts according to the disaster assessment level. The Comparison of the model predicted disaster area and the actual geological disaster areas was correlation (accuracy) of 75% or more. The study confirms that the gray clustering method to predict geological disasters is effective to reduce the loss of geological disasters and to prevent geological disasters.

Grey Clustering Model of Coating Erosion Resistance Evaluation

The erosion resistance evaluation of coating has important academic significance as well as project application value. Being the traditional way, the direct comparison method requires large amount of trivial work. By taking different coatings as clustering objects and data obtained from coating in erosion experiments as clustering index, this paper, on the other hand, defines the whitenization weight function of the index with grey clustering method, and has in this way built the grey clustering mathematical model of coating erosion resistance evaluation. As is proved in practice that the model makes it possible to evaluate the erosion resistance of coating scientifically, making the evaluation more reasonable and objective. At the same time, due to the accurate evaluation results, it avoids the subjectivity and one-sidedness otherwise being unavoidable for judging by experience. The coating erosion resistance experiments do not have strict demands for data volume, which makes the evaluation process both simple and scientific.