海量数据的支持向量机优化挖掘方法Support vector machine based optimization mining method of massive data
李清霞
摘要(Abstract)：
传统支持向量机挖掘方法可以对领域数据实现挖掘,但在复杂多变环境下数据挖掘离散程度较大。提出海量数据的支持向量机优化挖掘方法,构造静态粒子空间,局限海量数据挖掘离散程度,形成小规模的、多簇团的粒子挖掘数据集;将单粒子挖掘数据进行离散性拟合,以多簇团粒子整合离散运算,保证挖掘计算进行周期性运行;对同轨挖掘计算进行条件约束,实现小离散程度的数据挖掘。仿真实验验证结果表明,支持向量机优化挖掘方法在复杂多变环境下具有较高的稳定性,并且挖掘离散度小、挖掘信息精度较高。
关键词(KeyWords)： 海量数据;支持向量机;多簇团粒子;数据拟合;整合运算;挖掘离散;优化方法
基金项目(Foundation): 中国教师发展基金会(CTF120715);; 广东理工学院质量工程项目基金(JXGG2017023);广东理工学院精品资源共享课程项目基金(JPKC2016001)~~
作者(Author): 李清霞
DOI: 10.16652/j.issn.1004-373x.2018.06.033
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