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• W3182686523 abstract "The ID3 algorithm is a key and important method in existing data mining, and its rules are simple and easy to understand and have high application value. If the decision tree algorithm is applied to the online data migration of sports competition actions, it can grasp the sports competition rules in the relationship between massive data to guide sports competition. This paper analyzes the application performance of the traditional ID3 algorithm in online data migration of sports competition actions; realizes the application steps and data processing process of the traditional ID3 algorithm, including original data collection, original data preprocessing, data preparation, constructing a decision tree, data mining, and making a comprehensive evaluation of the traditional ID3 algorithm; and clarifies the problems of the traditional ID3 algorithm. Mainly, the problems of missing attributes and overfitting are clarified, which provide directions for the subsequent algorithm optimization. Then, this paper proposes a <math xmlns=http://www.w3.org/1998/Math/MathML id=M1> <mi>k</mi> </math> -nearest neighbor-based ID3 optimization algorithm, which selects values similar to <math xmlns=http://www.w3.org/1998/Math/MathML id=M2> <mi>k</mi> </math> -nearest neighbors to fill in the missing values for the attribute missing problem of the traditional ID3 algorithm. Based on this, the improved algorithm is applied to the online data migration of sports competition actions, and the application effect is evaluated. The results show that the performance of the <math xmlns=http://www.w3.org/1998/Math/MathML id=M3> <mi>k</mi> </math> -nearest neighbor-based ID3 optimization algorithm is significantly improved, and it can also solve the overfitting problem existing in the traditional ID3 algorithm. For the overall classification problem of six types of samples of travel patterns, the experimental data samples have the characteristics of high data quality, a considerable number of samples, and obvious sample differentiation. Therefore, this paper also uses the deep factorization machine algorithm based on deep learning to classify the six classes of travel patterns of sports competition action data using the previously extracted relevant features. The research in this paper provides a more accurate method and a higher-performance online data migration model for sports competition action data mining." @default.
• W3182686523 created "2021-07-19" @default.
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• W3182686523 date "2021-07-10" @default.
• W3182686523 modified "2023-09-26" @default.
• W3182686523 title "Online Data Migration Model and ID3 Algorithm in Sports Competition Action Data Mining Application" @default.
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• W3182686523 doi "https://doi.org/10.1155/2021/7443676" @default.
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