多元化全民健身效益指数异构数据挖掘模型

Heterogeneous Data Mining Model of National Fitness Benefit Index Diversification

通过对全民健身效益指数异构数据挖掘,分析全民健身体育运动训练对社会人群的健康效益指数的促进关系模型,长期实际考察和样本采集分析,掌握多元化全面健身运动对不同群体身体机能改进程度。传统的全民健身效益指数异构数据挖掘算法采用运动条件下身体功能参数指标体系构建算法,当不同运动群体的体质测定具有非显著性差异时,数据挖掘的效果不好。提出一种基于全民健身效益指数信息存储树形结构分形的多元化异构数据挖掘算法。考虑不同的健身项目类型,构建网格分配下的网络资源数据库结构模型,得到多元化全面健身中多叉树级联系统,基于高维多元统计分析方法,得到基于全民健身效益指数信息存储树形结构分形的挖掘模型,提高数据挖掘性能。仿真结果表明,该模型能提高数据挖掘算法检索和数据挖掘性能的可控性和普适性,精度较高,效益指数预测偏差降低,性能优越,实现了通过对全面健身效益指数的异构数据挖掘,定量分析全面健身活动对社会经济和人体身体素质及健康水平的促进作用。

Through mining the fitness benefit index of heterogeneous data analysis model, to promote the relationship between national fitness sports training on the social crowd health benefit index, long-term practical investigation and sample collection and analysis, to grasp the diversified comprehensive fitness degree of improvement on different groups of body function. The national fitness benefit index of heterogeneous data mining algorithm to construct the traditional physical .function parameter index system under the conditions of movement using the algorithm, when different exercise groups physical fitness measurement with non significant difference, the effect of data mining is not good. Proposes a mining algo- rithm diverse heterogeneous data fitness benefit index information storage tree structure based on fractal. Considering the different fitness project type is obained. The simulation results show that the model can improve the mining algorithm for retrieval and data mining performance data of the controllability and universality, high precision, efficiency index prediction error is reduced, superior performance, through the implementation of a comprehensive fitness benefit index of heterogeneous data mining, quantitative analysis of full fitness activities to promote the role to the social economy and the human body quality and health level.