In graph theory, the clustering coefficient (also known as clustering coefficient, clustering coefficient) is the coefficient used to describe the degree of clustering between the vertices of a graph. Specifically, it is the degree to which the adjacent points of a point are connected to each oth...
Clustering coefficient is a well-studied attribute in graph theory. It measures the degree to which nodes in a graph tend to cluster together. In this paper, we apply normalized clustering coefficient as a weighting scheme to construct weighted networks for supervised link prediction. Unlike the ...
Clustering中文翻译作"聚类",简单地说就是把相似的东西分到一组,同Classification(分类)不同,对于一个classifier ,通常需要你告诉它"这个东西被分为某某类"这样一些例子,理想情况下,一个classifier 会从它得到的训练集中进行"学习",从而具备对未知数据进行分类的能力,这种提供训练数据的过程通常叫做supervised learning(...
Graph theory is a branch of discrete mathematics that deals with the study of mathematical structures to model entities and relationships between them, in the form of nodes/vertices and relations/edges, respectively. It has been discussed by Pal Singh et al. [31], along with its wide spectrum...
(Additional file1: Fig. S11b), the configurations of 18 and 8 neighbors correspond to two-hop neighbors in graph theory. For STARmap and osmFISH technologies, which lack patterned unit organization, we performed ablation studies and found that 8 neighbors achieved the best ARI scores of 0.73 ...
Silhouette Coefficient has been widely used to measure clustering quality in the absence of ground truth. Nidheesh et al. [34] used the same index as the cost function in agglomerative clustering to select the best cluster to merge and also to estimate the correct number of clusters. Xie et...
coarse grid, and cellular gene expression was summed within each grid square to simulate spots capturing multiple cells. We calculated four indices, Pearson correlation coefficient (PCC), structural similarity index measure (SSIM), root mean squared error (RMSE), and Jensen–Shannon divergence (JSD)...
(iii) type of sf, for example, for fingerprints, the Euclidean metric is likely to produce a lot more ties than the Tanimoto coefficient and the cosine coefficient, which produces less. For continuous data, the number of ties depends on the number of possible measure values of each [Math ...
three different measures for clustering are often considered. The first is the so-called global clustering coefficient, defined as three times the ratio of the number of triangles to the number of paths of length two in the graph. The second is the local clustering coefficient, which for each...
For each data object, the sparse representation coefficient vector is computed by sparse representation theory and KNN algorithm is used to find the k nearest neighbors. Instead of using all the coefficients to construct the affinity matrix directly, we update each coefficient vector by remaining ...