Application of online data migration model and ID3 algorithm in sports competition data mining

To enhance the effectiveness of data mining in sports competitions, this study presents a sports competition data mining model based on the online data migration model and guided by machine learning principles. Additionally, a multi-source online migration learning algorithm that combines VFDT and local accuracy is proposed. Furthermore, a data stream classification algorithm is introduced that leverages multiple historical concept knowledge, and incorporates a dynamic classifier weight adjustment mechanism. This mechanism updates the classifier pool based on the weight, aiming to accommodate as many concepts as possible. When utilizing classifiers from the classifier pool for migration learning, the classification model can quickly adapt to new concepts. Moreover, a controlled experiment is designed to assess the performance of this algorithm. The research findings demonstrate that the algorithm proposed in this study is well-suited for data mining in sports competitions.