计算机科学 ›› 2021, Vol. 48 ›› Issue (3): 163-167.doi: 10.11896/jsjkx.200100046

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于等价关系的最小乐观概念格生成算法

温馨1, 闫心怡2, 陈泽华1   

  1. 1 太原理工大学大数据学院 太原030024
    2 太原理工大学电气与动力工程学院 太原030024
  • 收稿日期:2020-01-07 修回日期:2020-05-20 出版日期:2021-03-15 发布日期:2021-03-05
  • 通讯作者: 陈泽华(zehuachen@163.com)
  • 作者简介:wx20126106@163.com
  • 基金资助:
    国家自然科学基金(61703299); 国家重点研发计划资助(2018YFB1404500)

Minimal Optimistic Concept Generation Algorithm Based on Equivalent Relations

WEN Xin1, YAN Xin-yi2, CHEN Ze-hua1   

  1. 1 College of Big Data Science,Taiyuan University of Technology,Taiyuan 030024,China
    2 College of Electrical and Power Engineering,Taiyuan University of Technology,Taiyuan 030024,China
  • Received:2020-01-07 Revised:2020-05-20 Online:2021-03-15 Published:2021-03-05
  • About author:WEN Xin,born in 1993,postgraduate.Her main research interests include granular computing and knowledge engineering.
    CHEN Ze-hua,born in 1974,professor, is a senior member of China Computer Federation.Her main research interests include data mining and knowledge discovery.
  • Supported by:
    National Natural Science Foundation of China(61703299) and National Key R&D Program of China(2018YFB1404500).

摘要: 决策信息系统的规则提取是数据挖掘的研究内容之一,概念格理论与粒计算理论是该领域研究的主要数学工具。文中通过探究这两大理论间的关系,利用等价关系定义了最小乐观概念格及其结构,最小乐观概念区别于传统经典概念,但是具有格的结构。在此基础上,提出了一种决策信息系统的规则提取算法,该算法引入了粒度思想,通过求取每一粒层中的最小乐观概念,并根据最小乐观概念的外延与决策属性等价类间的蕴含关系进行决策规则提取,通过设置算法的终止条件来加快其收敛速度,以达到针对决策信息系统知识约简的目的。最小乐观概念的定义比经典概念的定义更宽泛,其生成过程也更简单。最后,通过理论证明、实例验证以及数值实验对比验证了该方法的正确性与优越性。

关键词: 概念格理论, 规则提取, 决策信息系统, 粒计算, 最小乐观概念

Abstract: Rule extraction of decision information system is an important topic in the field of data mining.Concept lattice theory and rough set theory are both theoretical tool for data analysis.This paper explores the relationship between these two theories,and uses the equivalent relationship to define the minimal optimistic concept lattice and its structure.The minimal optimistic concept is different from the traditional classic concept,but has a lattice structure,and a rule extraction algorithm for decision table is proposed.Based on granular computing,the algorithm computes the concepts in each layer from coarse to fine granularity space,and extracts decision rules according to the relationship between minimal optimistic concepts and decision equivalence classes.In order to achieve the purpose of knowledge reduction for decision information systems,the algorithm accelerates its convergent speed by setting the termination conditions.The definition of minimal optimistic concept is broader than classical concept,and the generation algorithm is simpler.The correctness and effectiveness of the new algorithm are verified by theorem proving and case analysis.Finally,the experimental results based on different data sets demonstrate that the proposed algorithm is more effective for rule extraction in most cases than other algorithms.

Key words: Concept lattice theory, Decision information system, Granular computing, Minimal optimistic concept, Rule extraction

中图分类号: 

  • TP181
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