Yadav, A. Rana K-means with three different distance metrics Int. J. Comput. Appl., 67 (10) (2013), pp. 13-17, 10.5120/11430-6785 View in ScopusGoogle Scholar Tu et al., 2019 B. Tu, N. Li, Z. Liao, X. Ou, G. Zhang Hyperspectral anomaly detection via spatial density back...
Further, there are a series of distance metrics that can be applied to calculate point-to-point distances. In this research, the K-means clustering algorithm is evaluated with three different mathematical metrics in terms of execution time with different datasets and different numbers of clusters. ...
fromscipy.cluster.hierarchyimportdendrogram,linkagefrommatplotlibimportpyplotasplt# 生成层次聚类的链接矩阵Z=linkage(X,method='ward')# 绘制树状图plt.figure(figsize=(10,5))plt.title('Hierarchical Clustering Dendrogram')plt.xlabel('Sample index')plt.ylabel('Distance')dendrogram(Z)plt.show() 上图展示了...
We use cluster integrity [30], the sum of the distance from the pattern to the center, to evaluate the performance. Table 1. Performance comparison of different algorithms on three datasets. 3.2. Experiments on Students’ Academic Data 3.2.1. Source of Students’ Academic Data The student’...
MI versus SNR of different modulation formats after 100 km transmission is shown in Figure 4a. The line with black diamonds is the Shannon capacity. We can find out that MI in IPM outperforms QAM modulation. Moreover, the larger the modulation order is, the closer the distance IPM is to ...
By default, kmeans uses the squared Euclidean distance (see 'Distance' metrics). D— Distances from each point to every centroid numeric matrix Distances from each point to every centroid, returned as a numeric matrix. D is an n-by-k matrix, where element (j,m) is the distance from ...
the'Replicates'name-value pair argument to test different solutions. When you specify more than one replicate,kmeansrepeats the clustering process starting from different randomly selected centroids for each replicate, and returns the solution with the lowest total sum of distances among all the ...
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[33]. Recently, there has been a trend towards exploring various nonlinear distance metrics as an alternative to Euclidean distance [31]. Bobrowski and Bezdek extended thek-means clustering algorithm by exploring two similarity measures with nonhyperelliptical topologies: the square of thel1norm and...
Due to the utilization of the Euclidean distance measure for clustering, the proposed KCGWO has not completely distinguished all of the clusters in the data. In order to prove the performance of the proposed KCGWO, two additional metrics, such as Mean Absolute Error (MAE) and Mean Squared ...