Integration of SOM and PCA for Analyzing Sports Economic Data and Designing a Management System

Abstract

Currently, sports economic data have attracted more attention because they normally exist with a high dimensionality manner that reflects the historical behavior and the potential decision trend of users or players. Traditional analysis techniques dealing with such kinds of data rely heavily on the empirical knowledge of the manager. With the development of data science, traditional experience-based knowledge barely meets the requirements of multiple features and high-dimensional data analysis. In this regard, machine learning-based data analysis techniques nowadays can give more importance to the process of extracting latent information hidden in chaotic data, which helps users make decisions and take appropriate actions. In this paper, we integrate principal component analysis (PCA) and a self-organizing map (SOM) to exploit the hidden features in the high-dimensional data. Specifically, PCA considers an orthogonal transformation operation to linearly transform the observed data to the low-dimensional one. SOM clusters the data by constructing a two-layered neural network without manual intervention and knowing the category in the training stage. The integration of PCA and SOM helps promote the research on pattern recognition and visualization of high-dimensional data. The experimental results obtained from economic data indicate the effectiveness of the combination of PCA and SOM.