JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2020, Vol. 55 ›› Issue (5): 1-12.doi: 10.6040/j.issn.1671-9352.c.2020.002

   

Optimization of class-attribute block in multi-granularity formal concept analysis

LI Jin-hai1,2, HE Jian-jun1,2, WU Wei-zhi3,4   

  1. 1. Data Science Research Center, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    2. Faculty of Science, Kunming University of Science and Technology, Kunming 650500, Yunnan, China;
    3. School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China;
    4. Key Laboratory of Oceanographic Big Data Mining and Application of Zhejiang Province, Zhejiang Ocean University, Zhoushan 316022, Zhejiang, China
  • Published:2020-05-06

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Abstract: The existing meso-granularity labeled method in the multi-granularity formal concept analysis is not able to realize the cross-granularity combination for the internal information of class-attribute blocks. Motivated by this problem, this paper divides the internal information of a class-attribute block into sub-classes based on the requirements of practical applications, and puts forward multi-granularity class-attribute blocks by means of combining the obtained sub-classes in a cross-granularity way. Then it analyzes the internal structure of multi-granularity class-attribute blocks, and reveals the mechanism of how to update decision implications when the multi-granularity class-attribute block becomes from coarse to fine or fine to coarse. The obtained results in this paper can further improve the theory and method of multi-level knowledge discovery with multi-granularity formal concept analysis.

Key words: granular computing, formal concept analysis, concept lattice, multi-granularity formal context, multi-granularity class-attribute block

CLC Number: 

  • TP18
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