南京大学学报(自然科学版) ›› 2019, Vol. 55 ›› Issue (4): 519–528.doi: 10.13232/j.cnki.jnju.2019.04.001

所属专题： 测试专题

• •    下一篇

属性的变化对于流图的影响

姚宁1,2,苗夺谦1,2(),张远健1,2,康向平1,2   

  1. 1. 同济大学计算机科学与技术系，上海，201804
    2. 同济大学嵌入式系统和服务计算教育部重点实验室，上海，201804
  • 收稿日期:2019-05-17 出版日期:2019-07-30 发布日期:2019-07-23
  • 通讯作者: 苗夺谦 E-mail:dqmiao@tongji.edu.cn
  • 基金资助:
    国家重点研发计划(213);公安部重大专项(20170004)

The impact of changing attributes on flow graph

Ning Yao1,2,Duoqian Miao1,2(),Yuanjian Zhang1,2,Xiangping Kang1,2   

  1. 1. Department of Computer Science and Technology, Tongji University, Shanghai, 201804, China
    2. Key Laboratory of Embedded System & Service Computing, Ministry of Education of China, Tongji University, Shanghai, 201804, China
  • Received:2019-05-17 Online:2019-07-30 Published:2019-07-23
  • Contact: Duoqian Miao E-mail:dqmiao@tongji.edu.cn

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摘要：

人类的认知中具有粒化特性，并且同一现象在不同粒度上具有不同的解释.流图为知识的一种表示形式，素有直观性、计算便捷性和并行处理等特征.以属性?值形式的信息系统作为研究对象，针对新属性的添加而诱导的粒度变化，研究流图在不同粒度上的具体演变.流图在新粒度上的有效性取决于所涉及的等价类的变化和Markov性质的成立.具体的，若新粒度上仅有部分等价类中的成员保持Markov性质成立，则粒度变化可将图形结构由一个粒度上的流图转化为新粒度上的用于构成完整流图的基本构件；若Markov性质在新粒度上不成立，则流图可被转化为新粒度上的与流图无关的结构；若新粒度上等价类中的每个成员皆满足Markov性质，则流图在新粒度上保持不变.流感病人信息系统在不同粒度上的具体分析进一步验证了理论结果.这些结论有助于理解和刻画知识与粒度之间的关系，为模拟人类学习和思维奠定基础.

关键词: 流 图, Markov性质, 等价类, 粒 度, 粗糙集

Abstract:

Granulation is an inherent property of human cognition and the same phenomenon has different interpretations at different granularities. Flow graph is treated as a form of knowledge representation,and is known for its intuitive formation,straightforward computation and parallel processing. Taking the attribute?value information system as the research object,this paper studies the specific changes of the flow graph at different granularities which are induced by adding the new attribute(s). The validity of the flow graph at a new granularity depends on the change of the equivalence classes involved and the establishment of the Markov property. Specifically,if only parts of elements of the equivalence class at the new granularity maintain the Markov property,the change in granularity will then cause the graphical structure to be transformed from a flow graph at one granularity to a basic component for a complete flow graph at this new granularity. If the Markov property does not hold at the new granularity,the flow graph will be transformed into a structure that is unrelated to flow graph at this new granularity. If every element of the equivalence class at the new granularity satisfies the Markov property,the flow graph at one granularity will then remain unchanged at this new granularity. The illustrations of an information system on patients suffering from flu at different granularities further validate the proposed theoretical results. These conclusions can help to understand and characterize the relationship between knowledge and granularity,and lay the foundation for simulating human learning and thinking.

Key words: flow graph, Markov property, equivalence class, granularity, rough set

中图分类号: 

  • TP18

表1

流感病人信息系统1"

PatientHeadache(H)Temperature(T)Flu(F)
p1nohighyes

p2

p3

p4

p5

p6

yes

yes

no

yes

no

high

very high

normal

high

very high

yes

yes

no

no

yes

表3

添加新属性Muscle?pain(or Headache)得到的"

PatientHeadache(H)Muscle?pain(M)Temperature(T)Flu(F)
p1noyeshighyes
p2yesnohighyes
p3yesyesvery highyes
p4noyesnormalno
p5yesnohighno
p6noyesvery highyes

表2

流感病人信息系统2"

Fact no.Muscle?pain(M)Temperature(T)

Flu

(F)

Patient name
1yeshighyesp1

2

3

4

5

no

yes

yes

no

high

very high

normal

high

yes

yes

no

no

p2

p3,p6

p4

p5

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