Basketball Big Data and Visual Management System under Metaheuristic Clustering

Abstract

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