This paper presents an information system that analyzes and visualizes sports and human activity data. Clus tering is used to divide data into groups; however, the wide variation in methods for data preprocessing and clustering makes it difficult to decide on appropriate methods. Thus, for the analysis of clustering methods, we comparatively evaluate methods for preprocessing the data in addition to the different methods for clustering. In addition, our sports analytics information system provides an approach that is able to assign athletes to a cluster based on their individual features and hence provides an individual training assessment compared to the clusters obtained on the data. The proposed visualization approach in comparison to a certain cluster offers an intuitive solution for assessing the goodness of fit.
© Copyright 2024 Proceedings of the 26th International Conference on Enterprise Information Systems (ICEIS 2024). Published by ICEIS. All rights reserved.