摘要
针对传统非侵入式负载数据协同挖掘算法的数据挖掘力度较小、挖掘深度较浅,且挖掘准确度较低的缺点,提出一种基于时空约束和小波设计的非侵入式负载数据协同挖掘算法。利用时空约束条件对收集的非侵入式负载数据进行筛选,初步整合需要挖掘的数据,并对整合后的负载数据进行集成学习计算,使所要挖掘的非侵入式负载数据具有相应的学习规则。设计恒模小波信号对计算后的数据进行协同挖掘CMA算法处理,完成负载数据挖掘。与传统算法进行对比实验测试,实验结果表明,所提数据协同挖掘算法的数据挖掘精度较高,挖掘深度明显增加,挖掘效果优于传统算法。
Aming at the traditional non-invasive load data collaborative mining algorithm has low data mining intensity,shallow data mining depth and low mining accuracy.For this,put forward a kind of based on the constraints of time and space and wavelet design of noninvasive load data mining algorithm together.Using time and space constraints on collection of noninvasive filtered load data,a preliminary integration requires data mining,and the load of consolidated data calculation of integrated learning to dig noninvasive load data with appropriate learning rules,the design of constant modulus wavelet signal CMA collaborative computed data mining algorithm processing,to achieve full load data mining.Compared with the traditional algorithm,the experimental results show that the proposed collaborative data mining algorithm has higher data mining accuracy,significantly increased mining depth,and better mining effect than the traditional algorithm.
作者
郑宪秋
ZHENG Xianqiu(Department of Information Engineering and Big Data Science,Shanxi Institute of Technology,Yangquan 045000,Shanxi,China)
出处
《西安工程大学学报》
CAS
2019年第6期643-648,共6页
Journal of Xi’an Polytechnic University
基金
山西省软科学研究计划项目(2018041033)
关键词
非侵入式
负载数据
协同挖掘
时空约束
小波设计
non-invasive
load data
collaborative mining
space-time constraints
wavelet design
