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...
Graph theory is a useful tool for deciphering structural and functional networks of the brain on various spatial and temporal scales. The clustering coefficient quantifies the abundance of connected triangles in a network and is a major descriptive statistics of networks. For example, it finds an ...
The clustering coefficient is defined as the probability that two neighboring vertices of a given vertex are also neighbors of each other, and may provide another useful feature to characterize instance difficulty for graph based problems like timetabling. ...
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(...
Min-hash approach:Min-hash approach attempts to define a node’s outlinks (hyperlinks) as sets, i.e. two nodes are considered similar, if they share many outlinks [73]. The jaccard coefficient is used to represent the similarity between two nodes (Baharav et al. [74]). ...
The square (Euclidean) distance, city block distance, correlation coefficient, and hamming distance are some common attribute dissimilarity functions (Murphy, 2012). For the k-means clustering algorithm in which k initial points are selected to represent the initial cluster centers, all data points ...
(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 ...
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)...
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 ...
Divisive-based clustering using vertex clustering coefficient can be one of the techniques to be investigated for clustering such large-scale networks. Outlier points’ impact on dimension reduction in the unsupervised learning setting needs also to be explored further. Estimating the number of clusters...