[1]张晓鹤,陈德刚,米据生.基于信息熵的对象加权概念格[J].智能系统学报,2020,15(6):1097-1103.[doi:10.11992/tis.202006043]
 ZHANG Xiaohe,CHEN Degang,MI Jusheng.Object-weighted concept lattice based on information entropy[J].CAAI Transactions on Intelligent Systems,2020,15(6):1097-1103.[doi:10.11992/tis.202006043]
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第15卷
期数:
2020年6期
页码:
1097-1103
栏目:
学术论文—人工智能基础
出版日期:
2020-11-05

文章信息/Info

Title:
Object-weighted concept lattice based on information entropy
作者:
张晓鹤1 陈德刚1 米据生2
1. 华北电力大学 控制与计算机工程学院, 北京 102200;
2. 河北师范大学 数学科学学院, 河北 石家庄 050024
Author(s):
ZHANG Xiaohe1 CHEN Degang1 MI Jusheng2
1. School of Control and Computer Engineering, North China Electric Power University, Beijing 102200, China;
2. College of Mathematics and Information Science, Hebei Normal University, Shijiazhuang 050024, China
关键词:
形式背景概念信息熵粒计算概念格决策规则权值数据挖掘
Keywords:
formal contextcontextinformation entropygranular computingconcept latticedecision rulesweight valuedata mining
分类号:
TP18;O236
DOI:
10.11992/tis.202006043
摘要:
在大数据时代,由于数据规模越来越大,导致构造概念格的难度越来越高。在能够客观反映数据隐藏信息的前提下需删除冗余对象及属性,降低数据规模,构造更为简单的概念格,从而便于用户更高效地获取知识。为避免主观因素,本文由形式背景中属性的信息熵来获取单属性权重,采用均值方法计算对象权重,并用标准差计算对象重要性偏差值。通过设定的属性权重、对象权重和对象重要度偏差阈值,构造对象加权概念格。通过实例验证了,该方法可有效删除冗余概念,简化概念格构造过程。
Abstract:
In the era of big data, it is becoming increasingly difficult to construct concept lattices due to the increasingly large scale of data. To objectively reflect hidden information, redundant objects and attributes should be deleted and data size should be reduced to construct simple concept lattices, thus, facilitating users to acquire knowledge efficiently. In this study, to prevent subjective factors, the information entropy of an attribute in the formal context is used to obtain a single attribute weight and the attribute weight of the object is, then, calculated using the mean value method and the importance deviation of the object is calculated by standard deviation. By setting the attribute weight, object weight, and object importance deviation threshold, an object-weighted concept lattice is constructed. An example is provided to verify the effectiveness of this method in removing redundant concepts and simplifying the construction of concept lattices.

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备注/Memo

备注/Memo:
收稿日期:2020-06-24。
基金项目:国家自然科学基金项目(12071131,62076088)
作者简介:张晓鹤,博士研究生,主要研究方向为概念格、关联规则挖掘;陈德刚,教授,博士生导师,主要研究方向为机器学习、数据挖掘。完成自然科学基金面上项目3项、数学天元基金1项,参加973课题1项。发表学术论文150余篇;米据生,教授,博士生导师,主要研究方向为粗糙集、粒计算、概念格、数据挖掘与近似推理。主持国家自然科学基金项目3项,教育部博士点基金项目1项。获得省级自然科学奖3项,发表学术论文130余篇
通讯作者:陈德刚.E-mail:zxhzxh93@126.com
更新日期/Last Update: 2020-12-25