基于机器学习模型的大数据挖掘优化算法  

Big Data Mining Optimization Algorithm Based on Machine Learning Model

作　　者：万晓燕[1] Wan Xiaoyan(Qingdao Vocational and Technical College of Hotel Management,Qingdao Shandong 266100,China)

机构地区：[1]青岛酒店管理职业技术学院

出　　处：《信息与电脑》2019年第21期68-69,共2页Information & Computer

摘　　要：大数据分析的方法有很多,借助机器学习模型对大数据进行挖掘优化是现今较为有效的方法。基于此,笔者提出基于机器学习模型的大数据挖掘优化算法,以机器学习技术为基础,建立分布式挖掘模型,通过计算数据相异度,完成数据挖掘中心的优化。实验结果表明,本设计算法在总计算量和预测准确度上均胜于传统算法,证明了方法的有效性和实用性。There are many methods for big data analysis.It is a more effective method to optimize big data by means of machine learning model.Based on this,a big data mining optimization algorithm based on machine learning model is proposed.Based on the machine learning technology,a distributed mining model is established,and the data mining center is optimized by calculating the data dissimilarity.The experimental results show that the proposed algorithm outperforms the traditional algorithm in both total computation and prediction accuracy,which proves the effectiveness and practicability of the proposed method.

关 键 词：机器学习模型 大数据 挖掘优化 差异 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论]