基于粒计算的粗决策规则抽取与约简

阎红灿; 张奉; 刘保相

doi:10.11959/j.issn.1000-436x.2016244

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基于粒计算的粗决策规则抽取与约简

学术论文 | 更新时间：2024-06-05

- 基于粒计算的粗决策规则抽取与约简
- Rough decision rules extraction and reduction based on granular computing
- 通信学报 2016年37卷第Z1期页码：30-35
- 作者机构：
  
  华北理工大学理学院，河北唐山 063000
- 作者简介：
  
  [ "阎红灿（1968-），女，河北保定人，博士，华北理工大学教授、硕士生导师，主要研究方向为 Web 语义检索和知识发现。" ]
  [ "张奉（1989-），男，河北邯郸人，华北理工大学硕士生，主要研究方向为信息处理数学模型及应用。" ]
  [ "刘保相（1957-），男，河北衡水人，华北理工大学教授、硕士生导师，主要研究方向为粗糙集理论及应用、模糊数据挖掘。" ]
- 基金信息：
  
  国家自然科学基金资助项目;TheNationalNaturalScienceFoundationofChina(61370168);河北省自然科学基金资助项目;The Natural Science Foundation of Hebei Province(F2014209238)
- DOI：10.11959/j.issn.1000-436x.2016244
  中图分类号： TP182
- 网络首发：2016-10，
  
  纸质出版：2016-10-25
- 稿件说明：
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阎红灿, 张奉, 刘保相. 基于粒计算的粗决策规则抽取与约简[J]. 通信学报, 2016,37(Z1):30-35.

Hong-can YAN, Feng ZHANG, Bao-xiang LIU. Rough decision rules extraction and reduction based on granular computing[J]. Journal on Communications, 2016, 37(Z1): 30-35.
阎红灿, 张奉, 刘保相. 基于粒计算的粗决策规则抽取与约简[J]. 通信学报, 2016,37(Z1):30-35. DOI： 10.11959/j.issn.1000-436x.2016244.

Hong-can YAN, Feng ZHANG, Bao-xiang LIU. Rough decision rules extraction and reduction based on granular computing[J]. Journal on Communications, 2016, 37(Z1): 30-35. DOI： 10.11959/j.issn.1000-436x.2016244.

摘要

规则挖掘是数据挖掘的一项重要研究内容，也是决策支持系统、人工智能和推荐系统等领域的研究热点，其中，属性约简和最小规则集合抽取是关键环节，尤其抽取效率决定了其可应用性。将粗糙集模型和粒计算理论应用于决策规则约简，通过粒化函数实现决策表的粒化，由粒隶属度和概念粒集构造算法生成初始概念粒集，进而根据概念粒的分辨算子进行属性约简，可视化的概念粒格实现决策规则提取。实验结果表明该方法更易计算机编程实现，比已有方法高效实用。

Abstract

Rule mining was an important research content of data mining

and it was also a hot research topic in the fields of decision support system

artificial intelligence

recommendation system

etc

where attribute reduction and minimal rule set extraction were the key links.Most importantly

the efficiency of extraction was determined by its application.The rough set model and granular computing theory were applied to the decision rule reduction.The decision table was granulated by granulation function

the grain of membership and the concept granular set construction algorithm gener-ated the initial concept granular set.Therefore

attribute reduction could be realized by the distinguish operator of concept granule

and decision rules extraction could be achieved by visualization of concept granule lattice.Experimental result shows that the method is easier to be applied to computer programming and it is more efficient and practical than the existing methods.

关键词

Keywords

references

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