Generally, the existing graph-based multi-view clustering models consists of two steps: (1) graph construction; (2) eigen-decomposition on the graph Laplacian matrix to compute a continuous cluster assignment matrix, followed by a post-processing algorithm to get the discrete one. However, both ...
Star2 master BranchesTags Code Folders and files Name Last commit message Last commit date Latest commit History 6 Commits utils DOGC.m README.md main.m Repository files navigation README DOGC Discrete Optimal Graph Clustering Created by Yudong Han, Lei Zhu, Zhiyong Cheng, Jingjing Li, Xiaobai...
Cell lines from all tumor types (n = 1100) are used for k-means clustering with the parameter “column_km” in the function “Heatmap” from the ComplexHeatmap (v2.18.0) Bioconductor package set to 6, based on elbow plot using the function “fviz_nbclust” from the factoextra (v...
(2004). Mono- and multi-fractal investigation of scaling properties in temporal patterns of seismic sequences. Chaos Solitons Fractals, 19, 1–15. Article Google Scholar Turcotte, D. L. (1997). Fractals and chaos in geology and geophysics (2nd ed.). Cambridge University Press. Book Google...
Electric power utilities must ensure a consistent and undisturbed supply of power, with the voltage levels adhering to specified ranges. Any deviation from these supply specifications can lead to malfunctions in equipment. Monitoring the quality of suppl
Complex Network Clustering by Multiobjective Discrete Particle Swarm Optimization Based on Decomposition Complex network clustering by multiobjective discreteparticle swarm optimization based on decomposition. Gong M,Cai Q,Chen X,Ma L. Evolutionary Computation,... M Gong,Q Cai,X Chen,... - 《IEEE ...
‘SCT’ assay specified. The FindNeighbors function was applied to construct a shared nearest neighbour graph for the data using the first 30 principal components. Clustering was performed using the FindClusters function, which utilizes the shared nearest neighbour graph from the previous step. Finally...
methods: the idea behind these methods is that both feature representations and cluster assignments are to be learned at the same time, leading to enhanced clustering performance; - Hybrid deep clustering methods: They combine elements from both sequential multi-step and joint deep clustering ...
clustering 6.2.5 Fracture trace density distribution 6.2.6 Local models 6.2.7 Consistency with scanline data 6.2.8 Outcrop model 6.3 Lineaments 6.4 Borehole data 6.4.1 Introduction 6.4.2 1d-fracture definition 6.4.3 Global analyses 6.4.4 Descriptive local analyses 7 The scaling model(s) 7.1 ...
This method addresses the above problem from a multi-variate perspective by sensing differential signals across healthy and unhealthy conditions. The healthy and unhealthy conditions are trained using neural learning by augmenting/ ceasing external vital data. The unification is performed using single-...