基于改进Apriori算法的运动员多属性训练数据挖掘模型构建及仿真  

Construction and Simulation of Multi-attributed Training Data Mining Model for Athletes Based on Improved Apriori Algorithm

作　　者：齐娇娇 QI Jiaojiao(Sports Institute,Baoji University of Arys and Sciences Sports Institute,Baoji 721007)

机构地区：[1]宝鸡文理学院体育学院,宝鸡721007

出　　处：《微型电脑应用》2018年第12期137-139,共3页Microcomputer Applications

摘　　要：对于运动员训练而言,其效果是否理想取决于诸多因素的共同影响。面对复杂的各项因素,如何对海量相关数据进行有效挖掘,从而发现各项数据间的内在关联,成为了该领域的研究重点。在数据挖掘技术迅猛发展的时代背景下,针对运动员多属性训练的数据挖掘算法展开研究,重点对经典Apriori算法进行了分析探讨,就其所存在的问题提出了改进方法,并通过仿真实验证明了改进Apriori算法的优越性能。The effects of athlete training are influenced by many common factors.Facing on these complex factors,how to effectively mine massive data and find out the inherent correlation between data has become the focus of research in this field. Under the background of rapid development of data mining technology,this paper studies training algorithm with multiple attribute data for athlete training by using an improved classical Apriori algorithm.The existing problems and improvement methods for the classical Apriori algorithm are analyzed,and simulation experiments prove the superior performance of the improved Apriori algorithm.

关 键 词：数据挖掘 APRIORI算法 DC_Apriori算法 

分 类 号：G807[文化科学—体育训练]