Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the S...Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.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
A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion proces...A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-determined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduction of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petrophysical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of reservoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layerboundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example.展开更多
This article proposes a novel stable clustering design method for hierarchical satellite network in order to increase its stability,reduce the overhead of storage and exert effective control of the delay performances ...This article proposes a novel stable clustering design method for hierarchical satellite network in order to increase its stability,reduce the overhead of storage and exert effective control of the delay performances based on a 5-dimensional vector model. According to the function of stability measureinent and owing to the limitation of minimal average routing table length, the hierarchical satellite network is grouped into separate stable connected clusters to improve destruction resistance and reconstruction ability in the future integrated network. In each cluster, redundant communication links with little contribution to network stability and slight influences on delay variation are deleted to satisfy the requirements for stability and connectivity by means of optimal link resources, and, also, the idea of logical weight is introduced to select the optimal satellites used to communicate with neighboring cluster satellites. Finally, the feasibility and effectiveness of the proposed method are verified by comparing it with the simulated performances of other two typical hierarchical satellite networks, double layer satellite constellation（DLSC） and satellite over satellite（SOS）.展开更多
大多数操作系统的安全防护主要依赖基于签名或基于规则的方法,因此现有大多数的异常检测方法精度较低。因此,利用贝叶斯模型为同类群体建模,并结合时间效应与分层原则,为用户实体行为分析(User and Entity Behavior Analytics,UEBA)研...大多数操作系统的安全防护主要依赖基于签名或基于规则的方法,因此现有大多数的异常检测方法精度较低。因此,利用贝叶斯模型为同类群体建模,并结合时间效应与分层原则,为用户实体行为分析(User and Entity Behavior Analytics,UEBA)研究提供精度更高的数据集。然后,将基于实际记录的用户行为数据与贝叶斯层级图模型推测出的数据进行比较,降低模型中的误报率。该方法主要分为两个阶段:在第1阶段,基于数据驱动的方法形成用户行为聚类,定义用户的个人身份验证模式;在第2阶段,同时考虑到周期性因素和分层原则,并通过泊松分布建模。研究表明,数据驱动的聚类方法在减少误报方面能够取得更好的结果,并减轻网络安全管理的负担,进一步减少误报数量。展开更多
针对城市电网故障早期阶段的隐蔽性、潜伏性特点,通过构建“集体离群点-故障模式”的度量规则,对电网系统中全等级异常电流波动信号进行层次聚类分析,将区域性潜在故障检测问题转换为挖掘故障信号数据中的集体离群点问题。为提高检测效...针对城市电网故障早期阶段的隐蔽性、潜伏性特点,通过构建“集体离群点-故障模式”的度量规则,对电网系统中全等级异常电流波动信号进行层次聚类分析,将区域性潜在故障检测问题转换为挖掘故障信号数据中的集体离群点问题。为提高检测效率,设计了一种基于不动点迭代法的层次聚类改进算法(Fixed point iteration based k-medoids,FPK-medoids),利用不动点较强的局部搜索能力提高聚类收敛速度。在测试数据集和实例数据集上进行实验,结果表明改进算法的收敛性能优于传统算法,检测模型能够精准识别电网中的区域性潜在故障。展开更多
文摘Purpose： To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach： We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare these methods. We offer the correct syntax to deactivate the similarity algorithm for clustering analysis within the hierarchical clustering module of SPSS. Findings： When one inputs co-occurrence matrices into the data editor of the SPSS hierarchical clustering module without deactivating the embedded similarity algorithm, the program calculates similarity twice, and thus distorts and overestimates the degree of similarity. Practical implications： We offer the correct syntax to block the similarity algorithm for clustering analysis in the SPSS hierarchical clustering module in the case of co-occurrence matrices. This syntax enables researchers to avoid obtaining incorrect results. Originality/value： This paper presents a method of editing syntax to prevent the default use of a similarity algorithm for SPSS＇s hierarchical clustering module. This will help researchers, especially those from China, to properly implement the co-occurrence matrix when using SPSS for hierarchical cluster analysis, in order to provide more scientific and rational results.
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A genetic algorithm-based joint inversion method is presented for evaluating hydrocarbon-bearing geological formations. Conventional inversion procedures routinely used in the oil industry perform the inversion processing of borehole geophysical data locally. As having barely more types of data than unknowns in a depth, a set of marginally over-determined inverse problems has to be solved along a borehole, which is a rather noise sensitive procedure. For the reduction of noise effect, the amount of overdetermination must be increased. To fulfill this requirement, we suggest the use of our interval inversion method, which inverts simultaneously all data from a greater depth interval to estimate petrophysical parameters of reservoirs to the same interval. A series expansion based discretization scheme ensures much more data against unknowns that significantly reduces the estimation error of model parameters. The knowledge of reservoir boundaries is also required for reserve calculation. Well logs contain information about layer-thicknesses, but they cannot be extracted by the local inversion approach. We showed earlier that the depth coordinates of layerboundaries can be determined within the interval inversion procedure. The weakness of method is that the output of inversion is highly influenced by arbitrary assumptions made for layer-thicknesses when creating a starting model (i.e. number of layers, search domain of thicknesses). In this study, we apply an automated procedure for the determination of rock interfaces. We perform multidimensional hierarchical cluster analysis on well-logging data before inversion that separates the measuring points of different layers on a lithological basis. As a result, the vertical distribution of clusters furnishes the coordinates of layer-boundaries, which are then used as initial model parameters for the interval inversion procedure. The improved inversion method gives a fast, automatic and objective estimation to layer-boundaries and petrophysical parameters, which is demonstrated by a hydrocarbon field example.
基金National Natural Science Foundation of China(60532030)
文摘This article proposes a novel stable clustering design method for hierarchical satellite network in order to increase its stability,reduce the overhead of storage and exert effective control of the delay performances based on a 5-dimensional vector model. According to the function of stability measureinent and owing to the limitation of minimal average routing table length, the hierarchical satellite network is grouped into separate stable connected clusters to improve destruction resistance and reconstruction ability in the future integrated network. In each cluster, redundant communication links with little contribution to network stability and slight influences on delay variation are deleted to satisfy the requirements for stability and connectivity by means of optimal link resources, and, also, the idea of logical weight is introduced to select the optimal satellites used to communicate with neighboring cluster satellites. Finally, the feasibility and effectiveness of the proposed method are verified by comparing it with the simulated performances of other two typical hierarchical satellite networks, double layer satellite constellation（DLSC） and satellite over satellite（SOS）.
文摘大多数操作系统的安全防护主要依赖基于签名或基于规则的方法,因此现有大多数的异常检测方法精度较低。因此,利用贝叶斯模型为同类群体建模,并结合时间效应与分层原则,为用户实体行为分析(User and Entity Behavior Analytics,UEBA)研究提供精度更高的数据集。然后,将基于实际记录的用户行为数据与贝叶斯层级图模型推测出的数据进行比较,降低模型中的误报率。该方法主要分为两个阶段:在第1阶段,基于数据驱动的方法形成用户行为聚类,定义用户的个人身份验证模式;在第2阶段,同时考虑到周期性因素和分层原则,并通过泊松分布建模。研究表明,数据驱动的聚类方法在减少误报方面能够取得更好的结果,并减轻网络安全管理的负担,进一步减少误报数量。
文摘针对城市电网故障早期阶段的隐蔽性、潜伏性特点,通过构建“集体离群点-故障模式”的度量规则,对电网系统中全等级异常电流波动信号进行层次聚类分析,将区域性潜在故障检测问题转换为挖掘故障信号数据中的集体离群点问题。为提高检测效率,设计了一种基于不动点迭代法的层次聚类改进算法(Fixed point iteration based k-medoids,FPK-medoids),利用不动点较强的局部搜索能力提高聚类收敛速度。在测试数据集和实例数据集上进行实验,结果表明改进算法的收敛性能优于传统算法,检测模型能够精准识别电网中的区域性潜在故障。
