@Article{iasc.2020.010110,
AUTHOR = {Xinbao Wang, Dawu Huang, Xuemin Zhao},
TITLE = {Design of the Sports Training Decision Support System Based on the  Improved Association Rule, the Apriori Algorithm},
JOURNAL = {Intelligent Automation \& Soft Computing},
VOLUME = {26},
YEAR = {2020},
NUMBER = {4},
PAGES = {755--763},
URL = {http://www.techscience.com/iasc/v26n4/40280},
ISSN = {2326-005X},
ABSTRACT = {In order to improve the judgment decision ability of the sports training effect, a 
design method of the sports training decision support system based on the 
improved association rule, the Apriori algorithm is proposed, and a phase space 
model of the sports training decision support data association rule distribution is 
constructed. The association rule mining method is used to support the data 
mining model of sports training, and the decision judgment of the sports 
training effect is carried out in the mixed cloud computing environment. The 
fuzzy information fusion and the data structure feature reorganization method 
is adopted, and the adaptive scheduling and information fusion of the sports 
training decision support data are realized. The judgment ability of the sports 
training decision support has improved, and the algorithm design of the sports 
training decision support system is carried out by using the association rule, the 
Apriori algorithm. The adaptive resource scheduling and feature recombination 
are used to improve the Apriori algorithm of the association rules. According to 
the results of the Apriori feature extraction of the association rules, the decision 
of the sports training is judged. The simulation results show that this method is 
used to design the sports training decision support system, which has a good 
mining performance and strong decision judgment ability for the sports training 
decision support vector set, and it has a good effect on the sports training 
decision making.},
DOI = {10.32604/iasc.2020.010110}
}