Aiming at the problem of low classification accuracy in traditional association data classification methods,a clas-sifying method for optimal association data in motion data is proposed. As the variable quantity of motion data is very large,and the change degree also can not be deduced,the optimal relevance data in motion data needs to be determined. The high-speed extraction is carried out by the relation of the optimal relational data. The data extracted by different guide function is adopted to perform a systematic classification. In order to ensure the effectiveness of the classification method of optimal correlation data in the extracted motion data,the comparison and simulation experiment is designed. The experimental data show that the classifica-tion method of optimal correlation data in the motion data can do systematic classification of the optimal association data in the motion data accurately.%针对传统关联数据分类方法一直存在分类精度差的问题,提出一种运动数据中的最优关联数据的分类方法.由于运动数据的变化量十分的庞大,同时变化程度也无法用规律进行推导,因此需要确定运动数据中的最优关联数据,通过最优关联数据的关联性进行高速提取,使用异导函数对提取的数据进行系统的分类.为了保证提出的运动数据中的最优关联数据的分类方法的有效性,设计对比仿真实验,通过实验数据表明,提出的运动数据中的最优关联数据的分类方法能够准确地对运动数据中的最优关联数据进行系统分类.
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