We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases...We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules. Due to the merged clusters around the center cluster, the clustering results show high accuracy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA) proposed in 2004. Experimental results show that CDC has better performance.展开更多
According to the relationship between Toona sinensis Roem stand volume,productivity and forest age,site conditions,stand density and other factors,through selecting 8 representative cities or counties,using standard i...According to the relationship between Toona sinensis Roem stand volume,productivity and forest age,site conditions,stand density and other factors,through selecting 8 representative cities or counties,using standard investigation and stem analysis method,this paper makes relatively systematic research about the Toona sinensis Roem plantation accumulation,productivity,and the relationship between them and site conditions.Through comparative analysis indicators of multiple site conditions,this paper expects to give a more comprehensive picture about the source of accumulation and productivity difference.展开更多
The numerical method for multi-dimensional integrals is of great importance, particularly in the uncertainty quantification of engineering structures. The key is to generate representative points as few as possible bu...The numerical method for multi-dimensional integrals is of great importance, particularly in the uncertainty quantification of engineering structures. The key is to generate representative points as few as possible but of acceptable accuracy. A generalized L2（GL2）-discrepancy is studied by taking unequal weights for the point set. The extended Koksma-Hlawka inequality is discussed. Thereby, a worst-case error estimate is provided by such defined GL2-discrepancy, whose dosed-form expression is available. The characteristic values of GL2-discrepancy are investigated. An optimal strategy for the selection of the representative point sets with a prescribed cardinal number is proposed by minimizing the GL2-discrepancy. The three typical examples of the multi-dimensional integrals are investigated. The stochastic dynamic response analysis of a nonlinear structure is then studied by incorporating the proposed method into the probability density evolution method. It is shown that the proposed method is advantageous in achieving tradeoffs between the efficiency and accuracy of the exemplified problems. Problems to be further studied are discussed.展开更多
文摘We propose a new clustering algorithm that assists the researchers to quickly and accurately analyze data. We call this algorithm Combined Density-based and Constraint-based Algorithm (CDC). CDC consists of two phases. In the first phase, CDC employs the idea of density-based clustering algorithm to split the original data into a number of fragmented clusters. At the same time, CDC cuts off the noises and outliers. In the second phase, CDC employs the concept of K-means clustering algorithm to select a greater cluster to be the center. Then, the greater cluster merges some smaller clusters which satisfy some constraint rules. Due to the merged clusters around the center cluster, the clustering results show high accuracy. Moreover, CDC reduces the calculations and speeds up the clustering process. In this paper, the accuracy of CDC is evaluated and compared with those of K-means, hierarchical clustering, and the genetic clustering algorithm (GCA) proposed in 2004. Experimental results show that CDC has better performance.
文摘According to the relationship between Toona sinensis Roem stand volume,productivity and forest age,site conditions,stand density and other factors,through selecting 8 representative cities or counties,using standard investigation and stem analysis method,this paper makes relatively systematic research about the Toona sinensis Roem plantation accumulation,productivity,and the relationship between them and site conditions.Through comparative analysis indicators of multiple site conditions,this paper expects to give a more comprehensive picture about the source of accumulation and productivity difference.
基金supported by the National Natural Science Foundation of China(Grant Nos.51538010&51261120374)the State Key Laboratory of Disaster Reduction in Civil Engineering(Grant No.SLDRCE14-B-17)the Fundamental Funding for Central Universities
文摘The numerical method for multi-dimensional integrals is of great importance, particularly in the uncertainty quantification of engineering structures. The key is to generate representative points as few as possible but of acceptable accuracy. A generalized L2（GL2）-discrepancy is studied by taking unequal weights for the point set. The extended Koksma-Hlawka inequality is discussed. Thereby, a worst-case error estimate is provided by such defined GL2-discrepancy, whose dosed-form expression is available. The characteristic values of GL2-discrepancy are investigated. An optimal strategy for the selection of the representative point sets with a prescribed cardinal number is proposed by minimizing the GL2-discrepancy. The three typical examples of the multi-dimensional integrals are investigated. The stochastic dynamic response analysis of a nonlinear structure is then studied by incorporating the proposed method into the probability density evolution method. It is shown that the proposed method is advantageous in achieving tradeoffs between the efficiency and accuracy of the exemplified problems. Problems to be further studied are discussed.
