通信学报 ›› 2016, Vol. 37 ›› Issue (Z1): 30-35.doi: 10.11959/j.issn.1000-436x.2016244

• 学术论文 • 上一篇    下一篇

基于粒计算的粗决策规则抽取与约简

阎红灿,张奉,刘保相   

  1. 华北理工大学理学院,河北 唐山 063000
  • 出版日期:2016-10-25 发布日期:2017-01-17
  • 基金资助:
    国家自然科学基金资助项目;河北省自然科学基金资助项目

Rough decision rules extraction and reduction based on granular computing

Hong-can YAN,Feng ZHANG,Bao-xiang LIU   

  1. College of Science,North China University of Science and Technology,Tangshan 063000,China
  • Online:2016-10-25 Published:2017-01-17
  • Supported by:
    TheNationalNaturalScienceFoundationofChina;The Natural Science Foundation of Hebei Province

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。

关键词: 粒计算, 粒隶属函数, 分辨算子, 概念粒格, 规则提取

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.

Key words: granular computing, membership function of grain, distinguish operator, concept granule lattice, rules ex-traction

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