Research on the Application of Data Mining Technology in the Analysis of College Students’ Sports Psychology

Abstract

The advent of the information age has changed every existing career and revolutionized most if not all fields, notwithstanding many benefits that came along with it. There has been an exponential rise in information and, alongside it, an increase in data. Data centers have erupted with details as the number of rows in databases grows by the day. The use of technology has nevertheless become essential in many company models and organizations, warranting its usage in virtually every channel. College physical education and sports are not an exception as students studying such subjects are skyrocketing. As the information is getting more complex, improved methods are needed to research and analyze data. Fortunately, data mining has come to the rescue. Data mining is a collection of analytical methods and procedures used exclusively for the sake of data extraction. It may be used to analyze features and trends from vast quantities of data. The objective of this study is to explore the use of data mining technologies in the analysis of college students’ sports psychology. This study uses clustering methods for the examination of sports psychology. We utilize three clustering methods for this aim: expectation-maximization (EM) algorithm, k-means, COBWEB, density-based clustering of applications with noise (DBSCAN), and agglomerative hierarchal clustering algorithms. We perform our forecasts based on various metrics combined with the past outcomes of college sports using these methods. In contrast to conventional data research and analysis techniques, our approaches have relatively high prediction accuracy as far as college athletics is concerned.