摘要
在大数据时代,越来越容易收集到大量样本,目前使用多个二元关系且可对混合型样本分类的已有方法较耗时.为克服这个不足,本文提出了两类局部邻域多粒度粗糙集模型,并研究了一些相关性质.通过算法和实例说明了所提出的模型的有效性.
In the era of big data, collecting a large number of samples is becoming more and more easy, but the existing method of using multiple binary- relations to classify the mixed samples is time-consuming. To conquer this shortage, this paper puts tbrward the two types of local neighborhood of muhigranularition rough set model, and some related properties are studied. The algorithm and example is given to illustrate the effectiveness of the proposed model.
作者
刘凤玲
林国平
LIU Fengling;LIN Guoping(School of Mathematics and Statistics,Minnan Normal University,Zhangzhou,Fujian 363000,China;Institute of Meteorological Big Data-Digital Fujian,Zhangzhou,Fujian 363000,China)
出处
《闽南师范大学学报（自然科学版）》
2018年第3期1-13,共13页
Journal of Minnan Normal University：Natural Science
基金
国家青年科学基金(61603173)
福建省自然科学基金(2016J01315)
浙江省海洋大数据挖掘与应用重点实验室开放课题(OBDMA201603)
闽南师大教改项目重点课题(JG201703)
关键词
邻域粗糙集
多粒度
局部邻域多粒度粗糙集
混合属性
neighborhood rough sets
multigranulation
local neighborhood multigranularition rough sets
heterogeneous attributes
