With the rapid development of big data, the scale of realistic networks is increasing continually. In order to reduce the network scale, some coarse-graining methods are proposed to transform large-scale networks into...With the rapid development of big data, the scale of realistic networks is increasing continually. In order to reduce the network scale, some coarse-graining methods are proposed to transform large-scale networks into mesoscale networks. In this paper, a new coarse-graining method based on hierarchical clustering (HCCG) on complex networks is proposed. The network nodes are grouped by using the hierarchical clustering method, then updating the weights of edges between clusters extract the coarse-grained networks. A large number of simulation experiments on several typical complex networks show that the HCCG method can effectively reduce the network scale, meanwhile maintaining the synchronizability of the original network well. Furthermore, this method is more suitable for these networks with obvious clustering structure, and we can choose freely the size of the coarse-grained networks in the proposed method.展开更多
For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cit...For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail.In this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is investigated.The indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure.The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China.According to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using K-means clustering algorithm.Then,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are proposed.The results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing countries.The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.展开更多
The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied throug...The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.展开更多
The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allo...The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.展开更多
Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closel...Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.展开更多
A hierarchical cluster-tendency （HCT） method in analyzing the group structure of networks of the global foreign exchange （FX） market is proposed by combining the advantages of both the minimal spanning tree （MST...A hierarchical cluster-tendency （HCT） method in analyzing the group structure of networks of the global foreign exchange （FX） market is proposed by combining the advantages of both the minimal spanning tree （MST） and the hierarchical tree （HT）. Fifty currencies of the top 50 World GDP in 2010 according to World Bank＇s database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be ＂colored＂ and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.展开更多
文摘With the rapid development of big data, the scale of realistic networks is increasing continually. In order to reduce the network scale, some coarse-graining methods are proposed to transform large-scale networks into mesoscale networks. In this paper, a new coarse-graining method based on hierarchical clustering (HCCG) on complex networks is proposed. The network nodes are grouped by using the hierarchical clustering method, then updating the weights of edges between clusters extract the coarse-grained networks. A large number of simulation experiments on several typical complex networks show that the HCCG method can effectively reduce the network scale, meanwhile maintaining the synchronizability of the original network well. Furthermore, this method is more suitable for these networks with obvious clustering structure, and we can choose freely the size of the coarse-grained networks in the proposed method.
基金supported by National Key Research and Development Program of China(Grant No.2018YFC0704903).
文摘For a city,analyzing its advantages,disadvantages and the level of economic development in a country is important,especially for the cities in China developing at flying speed.The corresponding literatures for the cities in China have not considered the indicators of economy and industry in detail.In this paper,based on multiple indicators of economy and industry,the urban hierarchical structure of 285 cities above the prefecture level in China is investigated.The indicators from the economy,industry,infrastructure,medical care,population,education,culture,and employment levels are selected to establish a new indicator system for analyzing urban hierarchical structure.The factor analysis method is used to investigate the relationship between the variables of selected indicators and obtain the score of each common factor and comprehensive scores and rankings for 285 cities above the prefecture level in China.According to the comprehensive scores,285 cities above the prefecture level are clustered into 15 levels by using K-means clustering algorithm.Then,the hierarchical structure system of the cities above the prefecture level in China is obtained and corresponding policy implications are proposed.The results and implications can not only be applied to the urban planning and development in China but also offer a reference on other developing countries.The methodologies used in this paper can also be applied to study the urban hierarchical structure in other countries.
文摘The problem of taking a set of data and separating it into subgroups where the elements of each subgroup are more similar to each other than they are to elements not in the subgroup has been extensively studied through the statistical method of cluster analysis. In this paper we want to discuss the application of this method to the field of education: particularly, we want to present the use of cluster analysis to separate students into groups that can be recognized and characterized by common traits in their answers to a questionnaire, without any prior knowledge of what form those groups would take (unsupervised classification). We start from a detailed study of the data processing needed by cluster analysis. Then two methods commonly used in cluster analysis are before described only from a theoretical point a view and after in the Section 4 through an example of application to data coming from an open-ended questionnaire administered to a sample of university students. In particular we describe and criticize the variables and parameters used to show the results of the cluster analysis methods.
文摘The paper deals with cluster analysis and comparison of clustering methods. Cluster analysis belongs to multivariate statistical methods. Cluster analysis is defined as general logical technique, procedure, which allows clustering variable objects into groups-clusters on the basis of similarity or dissimilarity. Cluster analysis involves computational procedures, of which purpose is to reduce a set of data on several relatively homogenous groups-clusters, while the condition of reduction is maximal and simultaneously minimal similarity of clusters. Similarity of objects is studied by the degree of similarity (correlation coefficient and association coefficient) or the degree of dissimilarity-degree of distance (distance coefficient). Methods of cluster analysis are on the basis of clustering classified as hierarchical or non-hierarchical methods.
基金support of the National Natural Science Foundation of China (No.51978601 and No.52161135202).
文摘Digital twin is regarded as the next-generation technology for the effective operation of heating,ventilation and air conditioning(HVAC)systems.It is essential to calibrate the digital twin models to match them closely with real physical systems.Conventional real-time calibration methods cannot satisfy such requirements since the computation loads are beyond acceptable tolerances.To address this challenge,this study proposes a clustering compression-based method to enhance the computation efficiency of digital twin model calibration for HVAC systems.This method utilizes clustering algorithms to remove redundant data for achieving data compression.Moreover,a hierarchical multi-stage heuristic model calibration strategy is developed to accelerate the calibration of similar component models.Its basic idea is that once a component model is calibrated by heuristic methods,its optimal solution is utilized to narrow the ranges of parameter probability distributions of similar components.By doing so,the calibration process can be guided,so that fewer iterations would be used.The performance of the proposed method is evaluated using the operational data from an HVAC system in an industrial building.Results show that the proposed clustering compression-based method can reduce computation loads by 97%,compared to the conventional calibration method.And the proposed hierarchical heuristic model calibration strategy is capable of accelerating the calibration process after clustering and saves 14.6%of the time costs.
文摘A hierarchical cluster-tendency （HCT） method in analyzing the group structure of networks of the global foreign exchange （FX） market is proposed by combining the advantages of both the minimal spanning tree （MST） and the hierarchical tree （HT）. Fifty currencies of the top 50 World GDP in 2010 according to World Bank＇s database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be ＂colored＂ and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.
