本文選題：數(shù)據(jù)挖掘 + 少年健康體育行為�。� 參考：《現(xiàn)代電子技術(shù)》2017年09期

【摘要】：采用大數(shù)據(jù)分析方法進(jìn)行少年健康體育行為統(tǒng)計(jì)分析,指導(dǎo)少年體育訓(xùn)練管理,提出基于數(shù)據(jù)挖掘技術(shù)的少年健康體育行為建模分析方法。首先采用模糊決策方法構(gòu)建體育行為特征的實(shí)體模型,結(jié)合支持向量機(jī)進(jìn)行體育行為大數(shù)據(jù)信息挖掘,構(gòu)建數(shù)據(jù)挖掘的統(tǒng)計(jì)決策目標(biāo)函數(shù);然后采用粒子群方法進(jìn)行挖掘目標(biāo)函數(shù)的參數(shù)尋優(yōu),實(shí)現(xiàn)對少年健康體育行為大數(shù)據(jù)準(zhǔn)確挖掘和特征分析。仿真結(jié)果表明,采用該方法進(jìn)行少年健康體育行為應(yīng)用分析,使體育關(guān)聯(lián)數(shù)據(jù)挖掘準(zhǔn)確度較高,統(tǒng)計(jì)分析的可靠性較好。
[Abstract]:The big data analysis method was used to analyze adolescent healthy sports behavior, to guide the management of juvenile sports training, and to put forward the modeling and analysis method of adolescent health sports behavior based on data mining technology. Firstly, the fuzzy decision method is used to construct the entity model of sports behavior characteristics, and the support vector machine (SVM) is used to mine sports behavior big data information, and the statistical decision objective function of data mining is constructed. Then the particle swarm optimization method is used to mine the parameters of the objective function to realize the accurate mining and feature analysis of the adolescent health sports behavior big data. The simulation results show that the application of this method to the analysis of adolescent health sports behavior makes the sports association data mining more accurate and the reliability of statistical analysis is better.
【作者單位】： 許昌學(xué)院體育學(xué)院;
【分類號】：G804.49;TP311.13

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