Neural network in sports cluster analysis

Abstract

In the era of rapid development of the Internet, various types of data in daily life are becoming more and more important, as are people's sports data. How to collect and store these massive exercise data and how to extract the user's exercise habits from these data are of great significance for improving people's exercise enthusiasm. This article mainly studies the application of the network in the cluster analysis of sports. This paper proposes a combination of a neural network-based sports data analysis model and a density peak clustering algorithm for unsupervised dimensionality reduction of high-dimensional data. We design both the encoder and the decoder with a three-layer fully connected neural network structure. The encoder extracts the characteristics of the sample data, and then, the decoder approximates the original input sample. The encoder is used to reduce the dimensionality of the high-dimensional data to the middle dimension, combined with the density peak clustering algorithm to further reduce the dimensionality, and then analyze the low-dimensional data. And set the corresponding learning rate for different data sets, the three data sets are iterated 40 times. The prediction accuracy rates of the algorithm in the three data sets are 94.1%, 90.3%, and 89.6% respectively; compared with the traditional PCA dimensionality reduction method, the method in this paper can extract data features more effectively, improve the dispersion between clusters, and have a better clustering effect. Finally, a numerical example is given to illustrate the effectiveness of the proposed algorithm.