多粒度云模型的相似性度量

doi:10.16451/j.cnki.issn1003-6059.201808001

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摘要

当前存在的云模型相似性度量仅局限于单粒度空间,缺乏多粒度云模型的相似性度量的相关研究.因此,文中首先证明知识距离框架的相关性质,并建立知识距离与信息度量、信息粒度之间的联系,在分层递阶粒结构上得到如下结论:同一粒结构中粒空间的粒度差异正相关于知识距离,通过知识距离可将随粒度连续变化的粒空间映射到一维坐标上.最后,在知识距离框架的基础上提出云模型相似性度量方法.实验验证上述结论在云模型粒空间上成立.

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	杨洁
	王国胤
	张清华
	冯林

关键词 ：云模型, 知识距离, 层次粒结构, 相似性度量

Abstract：

Traditional cloud similarity measures are only suitable for single granularity, and the multi-granularity cloud similarity measure is insufficient in research. In this paper, a knowledge distance framework is proposed and its relative properties are proved. The relationships between knowledge distance and information measure and information granularity are established. Moreover, two valuable conclusions are drawn in a hierarchical granular structure. The granularity difference of two granular spaces in a hierarchical granular structure is positive correlation to the knowledge distance framework between them. The granular spaces change continuously with the granularity and they can be mapped into the one-dimension coordinate. Finally, a similarity measure of cloud model based on the knowledge distance framework(KDFCM) is proposed. The experiments verify that the properties of KDFCM are consistent with the above conclusions.

Key words： Cloud Model Knowledge Distance Hierarchical Granular Structure Similarity Measure

收稿日期: 2018-04-15

ZTFLH:

TP 311

基金资助:

国家自然科学基金项目(No.61572091,61472056)、高端人才项目(No.RC2016005)、贵州省联合基金项目(No.黔科合LH字[2017]7075号)、四川省科技支撑计划项目(No.2015GZ0079)、省级重点学科(No.黔学科办[2013]18号)资助

通讯作者: 王国胤,博士,教授,主要研究方向为粗糙集、粒计算、智能信息处理、数据挖掘.E-mail:wanggy@cqupt.edu.cn.

作者简介: 杨洁,博士研究生,副教授,主要研究方向为云模型、粒计算、粗糙集、机器学习.E-mail:yj530966074@foxmail.com. 张清华,博士,教授,主要研究方向为粗糙集、模糊集、粒计算、三支决策.E-mail:zhangqh@cqupt.edu.cn. 冯林,博士,教授,主要研究方向为粗糙集、粒计算、机器学习.E-mail:839949655@qq.com.

引用本文:

杨洁, 王国胤, 张清华, 冯林. 多粒度云模型的相似性度量[J]. 模式识别与人工智能, 2018, 31(8): 677-692. YANG Jie, WANG Guoyin, ZHANG Qinghua, FENG Lin. Similarity Measure of Multi-granularity Cloud Model. , 2018, 31(8): 677-692.

链接本文:

http://manu46.magtech.com.cn/Jweb_prai/CN/10.16451/j.cnki.issn1003-6059.201808001 或 http://manu46.magtech.com.cn/Jweb_prai/CN/Y2018/V31/I8/677

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