[1]郑存芳,洪文学,李少雄,等.数据偏序结构关系中的知识发现可视化方法[J].智能系统学报,2016,11(4):475-480.[doi:10.11992/tis.201606019]
 ZHENG Cunfang,HONG Wenxue,LI Shaoxiong,et al.A novel knowledge discovery visualization method based on data partial ordered structure[J].CAAI Transactions on Intelligent Systems,2016,11(4):475-480.[doi:10.11992/tis.201606019]
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数据偏序结构关系中的知识发现可视化方法(/HTML)
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《智能系统学报》[ISSN:1673-4785/CN:23-1538/TP]

卷:
第11卷
期数:
2016年4期
页码:
475-480
栏目:
出版日期:
2016-07-25

文章信息/Info

Title:
A novel knowledge discovery visualization method based on data partial ordered structure
作者:
郑存芳12 洪文学1 李少雄1 任蕴丽13
1. 燕山大学 电气工程学院, 河北 秦皇岛 066004;
2. 燕山大学 里仁学院, 河北 秦皇岛 066004;
3. 河北科技师范学院 数学与信息科技学院, 河北 秦皇岛 066004
Author(s):
ZHENG Cunfang12 HONG Wenxue1 LI Shaoxiong1 REN Yunli13
1. School of Electrical Engineering, Yanshan University, Qinhuangdao 066004, China;
2. Liren College, Yanshan University, Qinhuangdao 066004, China;
3. College of Mathematics and Information Technology, Hebei Normal University of Science and Technology, Qinhuangdao 066004, China
关键词:
属性偏序决策图偏序结构形式概念分析可视化知识发现
Keywords:
decision diagram of attribute partial ordered structurepartial ordered structureformal concept analysisvisualizationknowledge discovery
分类号:
TP182
DOI:
10.11992/tis.201606019
摘要:
在形式概念分析与偏序结构理论基础上,针对决策模式信息表,提出一种基于认知原理的规则提取与知识发现的可视化新方法——属性偏序决策图。该方法在将决策问题转化为决策模式信息表的基础上,通过研究对象的属性特征,将其表现在可视化图形上,介绍了属性偏序结构图的原理、生成算法及应用实例。实验表明,属性偏序结构图可以将数据中蕴含的知识和规则得以形象地表示,通过对属性偏序决策图支路、节点、簇集的分析可以有效地发现数据中蕴含的决策规则。
Abstract:
In this paper, the formal concept is first analyzed and partial order structure theory introduced. The decision diagram of attribute partial ordered structure (DDAPOS), a visualization method of rule extraction and knowledge discovery based on cognitive principles, is then proposed. After the decision problem is transformed into a decision pattern information table, the attributes of a research object can be presented in the visualized diagram. This paper introduces the principles, generation algorithm, and application examples of DDAPOS. Experimental results show that the knowledge and rules contained in the data can be represented graphically, and the decision-making rules in the data can be found effectively through analysis of the graph branches, nodes and clusters.

参考文献/References:

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备注/Memo

备注/Memo:
收稿日期:2016-06-06。
基金项目:国家自然科学基金项目(61273019,61473339,61501397);河北省自然科学基金重点项目(F2016203443);燕山大学青年教师自主研究计划课题(13LGB033).
作者简介:郑存芳,男,1979年生,讲师,博士研究生,CAAI粗糙集与软计算专委会会员、CCF会员,主要研究方向为可视化模式识别、偏序结构理论、中医工程学等。先后参与国家自然科学基金3项、河北省自然科学基金3项;洪文学,男,1953年生,教授,博士生导师,燕山大学生物医学工程研究所所长,CAAI粗糙集与软计算专委会委员,主要研究方向为大数据偏序结构理论、复杂概念网络、混合数据信息融合与模式识别和中医工程学。所带领的学术团队近年来主持省部级科研项目20余项;李少雄,男,1987年生,博士研究生,主要研究方向为偏序结构理论、可视化模式识别、中医工程学等。参与国家自然科学基金项目4项。
通讯作者:洪文学.E-mail:hongwx@ysu.edu.cn.
更新日期/Last Update: 1900-01-01