id	author	title	date	pages	extension	mime	words	sentences	flesch	summary	cache	txt
work_3fky2fv4l5b7horsv5frlbujja	Santiago-Omar Caballero-Morales	Solution strategy based on Gaussian mixture models and dispersion reduction for the capacitated centered clustering problem	2021	19	.pdf	application/pdf	8453	1248	69	Keywords Capacitated centered clustering problem, Gaussian mixture models, Dispersion Most of the solving algorithms for the CCCP perform clustering within the search � At each iteration, Gaussian distribution-based clustering performs, for a given point, a values on the computations of GMMs. In order to reduce dispersion of the CCCP data the compression algorithm presented in EM algorithm (see Fig. 2), the number of points assigned to each cluster is obtained. Figure 4 Clustering with (A) standard EM algorithm and (B) DRG meta-heuristic (modified EM Table 2 presents the best results of the DRG meta-heuristic for the considered instances. Table 2 Performance of DRG, VNS, SA, CS, TS and GA on CCCP instances when compared to updated Best-known solutions*: (a) MATLAB, Solution strategy based on Gaussian mixture models and dispersion reduction for the capacitated centered clustering problem Solution strategy based on Gaussian mixture models and dispersion reduction for the capacitated centered clustering problem	./cache/work_3fky2fv4l5b7horsv5frlbujja.pdf	./txt/work_3fky2fv4l5b7horsv5frlbujja.txt