Between-group differences in the mean clustering coefficient C, normalized clustering coefficient gamma, the shortest path length L, and normalized shortest path length lambda over a range of sparsity v...
0.95 Drainage density ( Dd) 0.02 0.13 0.06 0.22 0.15 0.31 Population density ( Popd) 0.12 0.13 0.12 0.07 0.08 0.14 Specific discharge (Q s) 0.01 0.23 0.04 0.14 0.07 0.30 Temperature (T) 0.11 0.10 0.07 0.11 0.07 0.06 Table 5. Clustering results for GWR model coefficients....
Cluster analysis The GWR model generated a large number of results which provides a challenge for interpretation74. Therefore, based on GWR results, a clustering analysis usually served to further scrutinize the results. Two-step cluster method used in this study is a clustering method that determin...
It can be seen from Table 1 that although the number of electrodes changes, the image error is still large, and the correlation coefficient is relatively small. It makes sense that the size information of the measured object may be directly reflected by the capacitance value data obtained ...
Finally, the coefficient of determination (R2) and Root Mean Square Error (RMSE) between the modeled NSTLR and the observed NSTLR were calculated to evaluate the accuracy of the modeled NSTLR. The mean values of R2 between DEM and NLST were improved 0.3, 0.42 and 0.35, rather than between...
Image-Segmentation-by-Correlation-CLustering 图像分割相关聚类 使用的库: VLFeat 的 SLIC 超像素,SVM 训练 代码结构2.1 训练文件: superPixel.m - 初始超像素生成代码construct_superPixelGraph.m - 从超像素构建成对超像素图。 featureExtraction.m - 提取图中相邻超像素之间的成对特征。 ground_truth_by_maximum...
zscore1242112zscorePurposeStandardized scoreSyntaxZzscore DescriptionZzscore eachcolumn fromits mean normalized itsstandard deviation columnvector zte
Normalized rankings across networks, by: (A) degree centrality, (B) betweenness centrality, and (C) local clustering coefficient.Amir RostamiHernan Mondani
Kaufhold B., Kirlin R. L., Dizaji R. "Blind system identification using normalized Fourier coefficient gradient vectors obtained from time-frequency entropy-based blind clustering of data wavelets ", Digital Signal Processing Journal, Vol. 9, pp. 18-35, 1999...
The algorithm employs secondary directed differential, hierarchy, normalized density, as well as the self-adaption coefficient, and thus is called Structure Detecting Cluster by Hierarchical Secondary Directed Differential with Normalized Density and Self-Adaption, dubbed by SDC-HSDD-NDSA. The algorithm ...