Basketball Big Data and Visual Management System under Metaheuristic Clustering
نویسندگان
چکیده
This study aims to discuss the application value of KMC algorithm optimized by heuristic method in basketball big data analysis and visual management. Because is too complicated incomplete, extraction information not direct effective enough. Based on metaheuristic K-Means clustering (KMC) algorithm, weights genetic are introduced optimize it, University California at Irvine (UCI) set applied analyze performance algorithm. The 2018-2019 season National Basketball Association (NBA) shooting guards selected as research objects, used process NBA scoring functional factors. It found that number clusters increased from 2 16. After optimization, Between-Within Proportion (BWP) only drops 0.35, improved BWP (IBWP) 0.288, which shows smallest drop among all algorithms. When nodes 4, running time for processing COVTYPE 1922 s after IRIS shortest (113 s). parallel 10, speedup ratio 4.16, maximal expansion rate 0.81. accuracy traditional 89.33%. 98.67%. leader factor, offensive contribution stability passing ability factor core grouping maximum, 0.59, 0.51, 0.47, 0.43, respectively. has been shown reduce iterations, convergence time, improve accuracy. conclusion this can provide reference basis
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ژورنال
عنوان ژورنال: Mobile Information Systems
سال: 2022
ISSN: ['1875-905X', '1574-017X']
DOI: https://doi.org/10.1155/2022/2546418