Grey Relational Clustering Research Articles

China’s tropical cyclone disaster risk source analysis based on the gray density clustering

In recent years, tropical cyclones on the Pacific Northwest have decreased. We cannot infer that tropical cyclones impact China have reduced, because the Pacific Northwest is not homogeneous, and the variation characteristics of tropical cyclones in different sea areas are not clear. This paper uses gray relational density clustering algorithm to cluster tropical cyclone data sets between 1949 and 2008, according to the generated position of tropical cyclones, generated density and the possibility of landing. The Pacific Northwest is divided into different sea areas. Then, we analyze the risk of tropical cyclones generated in these sea areas. The results show that the probability of tropical cyclones landing generated in some sea areas is very high, reached 74 %, but the probability of tropical cyclones landing generated in other sea areas is only 2 %. Tropical cyclones generated in some sea areas are more likely to develop into typhoons, strong typhoons and so on, but the intensity of tropical cyclones generated in other sea areas is lower, there is little risk for China. Finally, according to the climate change stage trends, we divide the period 1949–2008 into three stages and analyze the tropical cyclone risk of each sea areas.

Applying hierarchical grey relation clustering analysis to geographical information systems – A case study of the hospitals in Taipei City

Deng proposed grey clustering analysis (GCA) in 1987. Later, Jin presented a new method in 1993, called grey relational clustering (GRC) method that combined grey relational analysis with clustering. However, the GRC method cannot use a tree diagram to make appropriate classification decisions without re-computation. This study thus attempts to combine GRC and hierarchical clustering analysis. Given the existence of an excess of medical resources in the Taipei area, this study attempts to understand the degree of concentration of medical resources in this area. Specifically, this study applies a geographical information system (GIS) to present the geographical distribution of hospitals in Taipei. Additionally, a new-type of cluster analysis, known as hierarchical grey relation clustering analysis, is used to analyze the distribution of hospitals and understand how they compete with one another. The analytical results demonstrate that hierarchical grey relation clustering analysis is a suitable method of analyzing geographical position. Tree diagrams can help policymakers make appropriate classification decisions without re-computation. The study results can inform hospitals of their competitors and help them to develop appropriate responses. Additionally, the analytical results can also provide a reference to government or hospital policymakers to help them position hospitals in areas, thus achieving a better distribution of medical resources in Taipei.

Research for RBF Neural Networks Modeling Accuracy of Determining the Basis Function Center Based on Clustering Methods

The radial basis function (RBF) neural network is superior to other neural network on the aspects of approximation ability, classification ability, learning speed and global optimization etc., it has been widely applied as feedforward networks, its performance critically rely on the choice of RBF centers of network hidden layer node. K-means clustering, as a commonly method used on determining RBF center, has low neural network generalization ability, due to its clustering results are not sensitive to initial conditions and ignoring the influence of dependent variable. In view of this problem, fuzzy clustering and grey relational clustering methods are proposed to substitute K-means clustering, RBF center is determined by the results of fuzzy clustering or grey relational clustering, and some researches of RBF neural networks modeling accuracy are done. Practical modeling cases demonstrate that the modeling accuracy of fuzzy clustering RBF neural networks and grey relational clustering RBF neural networks are significantly better than K-means clustering RBF neural networks, applying of fuzzy clustering or grey relational clustering to determine the basis function center of RBF neural networks hidden layer node is feasible and effective.

A Dynamic Assessment Method for Urban Eco‐environmental Quality Evaluation

ABSTRACTWith the rapid development of urbanization, urban environmental problems become a big challenge for urban socio‐economic development planning. In this paper, a new Dynamic Assessment method, which integrates technique for order preference by similarity to ideal solution, Entropy weight for time series data analysis, Grey relational analysis and Clustering, is proposed for urban eco‐environmental quality assessment of the Yangtze River Delta and Pearl River Delta in China. A sensitivity analysis is also conducted to identify the influence of weight or value changes of factors. The urban eco‐environmental quality ranking by the proposed method is consistent with the Chinese city competitiveness rankings in 2009 by the Chinese Academy of Social Sciences. It is much easier and more efficient to obtain the urban eco‐environmental quality ranking by the proposed method than by field studies and surveys. Copyright © 2011 John Wiley & Sons, Ltd.

Analysis of infrared images based on grey system and neural network

PurposeThe purpose of this paper is to develop a system to analyse the characteristics of infrared objects.Design/methodology/approachAccording to the gray scale of image pixel by the image entropy, gray scale estimating is carries on to construct the neural networks. And then the grey relational analysis and grey clustering methods are applied to filter the possible object. The target is predicted through image segmentation pretreatment based on the forecasting value by grey system and assigned corresponding mark. The forecasting precision is greatly elevated by GM (1, 1) model.FindingsThe paper illustrates that, based on the analysis and its experimental results, this system has a good recognition rate for infrared target.Research limitations/implicationsThis paper provides a way to grasp the minutial feature of the image. The filtering operation based on pixel level provided auto‐adapted filtering with a new stage.Practical implicationsApplications of grey theory deepened the content of detecting infrared targets and enriched technology of image processing.Originality/valueThis system introduces an effective method for detecting infrared targets.

Comprehensive evaluation of multiple cropping systems on upland red soil

According to the principles and methods of ecology and system engineering, we set up an evaluation indicator system for multi-component and multiple cropping systems, evaluated the comprehensive benefits of multi-component and multiple cropping systems using grey relation clustering analysis and screened out the optimized model based on research done in the upland red soil in Jiangxi Agricultural University from 1984 to 2004. The results show that the grey relation degree of “cabbage/potato/maize — sesame” was the highest among 23 multi-component and multiple cropping systems and was clustered into the optimized system. This indicates that “cabbage/potato/maize — sesame” can bring the best social, economic and ecological benefits, increase product yield and farmers’ income and promote sustainable development of agricultural production. Therefore, it is suitable for promotion on upland red soil. The grey relation degree of “canola/Chinese milk vetch/maize/mung bean/maize” was second, which is suitable for implementation at the city outskirts. In conclusion, these two planting patterns are expected to play important roles in the reconstruction of the planting structure and optimization of the planting patterns on upland red soil.

GDME: Grey Relational Clustering Applied to a Clock Tree Construction with Zero Skew and Minimal Delay

This study has demonstrated that the clock tree construction in an SoC should be expanded to consider the intrinsic delay and skew of each IP's clock sink. A novel algorithm, called GDME, is proposed to combine grey relational clustering and DME approach for solving the problem of clock tree construction. Grey relational analysis can cluster the best pair of clock sinks and that guide a tapping point search for a DME algorithm for constructing a clock tree with zero skew and minimal delay. Experimentally, the proposed algorithm always obtains an RC-or RLC-based clock tree with zero skew and minimal delay for all the test cases and benchmarks. Experimental results demonstrate that the GDME improves up to 3.74% for total average in terms of total wire length compared with other DME algorithms. Furthermore, our results for the zero-skew RLC-based clock trees compared with Hspice are 0.017% and 0.2% lower for absolute average in terms of skew and delay, respectively.

5241652 System for performing rule partitioning in a rete networkWilliam Barabash, William Yerazunis assigned to Digital Equipment Corporation

Deng proposed grey clustering analysis (GCA) in 1987. Later, Jin presented a new method in 1993, called grey relational clustering (GRC) method that combined grey relational analysis with clustering. However, the GRC method cannot use a tree diagram to make appropriate classification decisions without re-computation. This study thus attempts to combine GRC and hierarchical clustering analysis. Given the existence of an excess of medical resources in the Taipei area, this study attempts to understand the degree of concentration of medical resources in this area. Specifically, this study applies a geographical information system (GIS) to present the geographical distribution of hospitals in Taipei. Additionally, a new-type of cluster analysis, known as hierarchical grey relation clustering analysis, is used to analyze the distribution of hospitals and understand how they compete with one another. The analytical results demonstrate that hierarchical grey relation clustering analysis is a suitable method of analyzing geographical position. Tree diagrams can help policymakers make appropriate classification decisions without re-computation. The study results can inform hospitals of their competitors and help them to develop appropriate responses. Additionally, the analytical results can also provide a reference to government or hospital policymakers to help them position hospitals in areas, thus achieving a better distribution of medical resources in Taipei.