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《Zhejiang Sport Science》 2016-05
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Application of Data Mining Technology in Volleyball Athletes' Biochemical Indexes Analysis

MA Jing;LIU Gong-ju;Scientific Research Department,Zhejiang College of Sports;  
Objective:Analyze the relationship between biochemical indexes and the grade of athletes with data mining technology.Explore the practicability of using decision tree and association rule in volleyball athletes' biochemical indexes analysis.Methods:We applied C5.0and Apriori algorithm for analyzing 30 volleyball female athletes' biochemical data,with the IBM SPSS Modeler 14.1software.Results:A decision tree with 11 levels is built,and the model's prediction accuracy is 86%.6association rules are found.Conclusion:C5.0and Apriori algorithm can be used for the prediction and regularity analysis of the volleyball female athletes' grade.Creatine kinase is the most important index for the decision tree,and the testosterone and cortisol are in the next place.Testosterone is the most important index for association rule analysis.
【CateGory Index】: TP311.13
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