This paper describes the program module ＂GRAPHS＂ which was developed for data processing in geobotany and ecology fields. The ＂GRAPHS＂ has a simple interface and is integrated into the Microsoft Excel. This allows...This paper describes the program module ＂GRAPHS＂ which was developed for data processing in geobotany and ecology fields. The ＂GRAPHS＂ has a simple interface and is integrated into the Microsoft Excel. This allows users to use all features of Microsoft Excel for storage and preparation data for analysis. Calculation of the most common similarity indexes （Jaccarda. Sorenson, Ohai etc.） and their visualization by using different algorithms of the graph theory or hierarchical cluster analysis allows simplifying and accelerating the process of data analysis in ecology or geobotany and makes it clearer. Also, three ordination methods--PCA （principal components analysis）, CA （correspondence analysis）. NMS （nonmetric multidimensional scaling）-were implemented in the module. The module can be used for vegetation classification, and be used to allocate diagnostic species or to search environmental factors most strongly impact on vegetation. Algorithms of data analysis which were implemented in the module ＂GRAPHS＂ have universal nature so they can be applied in many other fields of science.展开更多
The clustering of objects(individuals or variables)is one of the most used approaches to exploring multivariate data.The two most common unsupervised clustering strategies are hierarchical ascending clustering(HAC)and...The clustering of objects(individuals or variables)is one of the most used approaches to exploring multivariate data.The two most common unsupervised clustering strategies are hierarchical ascending clustering(HAC)and k-means partitioning used to identify groups of similar objects in a dataset to divide it into homogeneous groups.The proposed topological clustering of variables,called TCV,studies an homogeneous set of variables defined on the same set of individuals,based on the notion of neighborhood graphs,some of these variables are more-or-less correlated or linked according to the type quantitative or qualitative of the variables.This topological data analysis approach can then be useful for dimension reduction and variable selection.It’s a topological hierarchical clustering analysis of a set of variables which can be quantitative,qualitative or a mixture of both.It arranges variables into homogeneous groups according to their correlations or associations studied in a topological context of principal component analysis(PCA)or multiple correspondence analysis(MCA).The proposed TCV is adapted to the type of data considered,its principle is presented and illustrated using simple real datasets with quantitative,qualitative and mixed variables.The results of these illustrative examples are compared to those of other variables clustering approaches.展开更多
提出一种识别输电断面的快速方法,为有针对性地实施输电断面的保护与控制策略提供前提。首先根据电网实时动态监测系统(Wide area measurement system,WAMS)提供的广域电压相角信息,采用基于离差平方和法的聚类分析方法,将待搜索电网或...提出一种识别输电断面的快速方法,为有针对性地实施输电断面的保护与控制策略提供前提。首先根据电网实时动态监测系统(Wide area measurement system,WAMS)提供的广域电压相角信息,采用基于离差平方和法的聚类分析方法,将待搜索电网或区域电网内母线分为2群;进一步基于图论中极大连通子图的概念,给出了在母线群内搜索电源区或负荷区的图论方法;最后将相邻电源区和负荷区间的联络线簇识别为输电断面。在此基础上,给出了输电断面的层次递进搜索策略及其停止准则,实现各级区域电网间和区域电网内输电断面的搜索。仿真结果验证了所提方法的有效性。展开更多
文摘This paper describes the program module ＂GRAPHS＂ which was developed for data processing in geobotany and ecology fields. The ＂GRAPHS＂ has a simple interface and is integrated into the Microsoft Excel. This allows users to use all features of Microsoft Excel for storage and preparation data for analysis. Calculation of the most common similarity indexes （Jaccarda. Sorenson, Ohai etc.） and their visualization by using different algorithms of the graph theory or hierarchical cluster analysis allows simplifying and accelerating the process of data analysis in ecology or geobotany and makes it clearer. Also, three ordination methods--PCA （principal components analysis）, CA （correspondence analysis）. NMS （nonmetric multidimensional scaling）-were implemented in the module. The module can be used for vegetation classification, and be used to allocate diagnostic species or to search environmental factors most strongly impact on vegetation. Algorithms of data analysis which were implemented in the module ＂GRAPHS＂ have universal nature so they can be applied in many other fields of science.
文摘The clustering of objects(individuals or variables)is one of the most used approaches to exploring multivariate data.The two most common unsupervised clustering strategies are hierarchical ascending clustering(HAC)and k-means partitioning used to identify groups of similar objects in a dataset to divide it into homogeneous groups.The proposed topological clustering of variables,called TCV,studies an homogeneous set of variables defined on the same set of individuals,based on the notion of neighborhood graphs,some of these variables are more-or-less correlated or linked according to the type quantitative or qualitative of the variables.This topological data analysis approach can then be useful for dimension reduction and variable selection.It’s a topological hierarchical clustering analysis of a set of variables which can be quantitative,qualitative or a mixture of both.It arranges variables into homogeneous groups according to their correlations or associations studied in a topological context of principal component analysis(PCA)or multiple correspondence analysis(MCA).The proposed TCV is adapted to the type of data considered,its principle is presented and illustrated using simple real datasets with quantitative,qualitative and mixed variables.The results of these illustrative examples are compared to those of other variables clustering approaches.
文摘提出一种识别输电断面的快速方法,为有针对性地实施输电断面的保护与控制策略提供前提。首先根据电网实时动态监测系统(Wide area measurement system,WAMS)提供的广域电压相角信息,采用基于离差平方和法的聚类分析方法,将待搜索电网或区域电网内母线分为2群;进一步基于图论中极大连通子图的概念,给出了在母线群内搜索电源区或负荷区的图论方法;最后将相邻电源区和负荷区间的联络线簇识别为输电断面。在此基础上,给出了输电断面的层次递进搜索策略及其停止准则,实现各级区域电网间和区域电网内输电断面的搜索。仿真结果验证了所提方法的有效性。
