JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2017, Vol. 52 ›› Issue (7): 1-12.doi: 10.6040/j.issn.1671-9352.0.2017.279

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Granular computing approach for formal concept analysis and its research outlooks

LI Jin-hai1,2, 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
  • Received:2017-06-05 Online:2017-07-20 Published:2017-07-07

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Abstract: Formal concept analysis is a useful mathematical method for knowledge representation and processing and its key tool is concept lattice. However, the construction of concept lattice takes exponential time complexity, which to some extent makes data processing inefficient and hinders fast development of this theory and its application. Granular computing is well-known for formation of granule, transformation of granule, and synthesis and decomposition of granule. Granular computing allows to consider problem by granularity in various levels, and strikes a balance between accuracy and time consuming in solving problem based on the practical requirements. The main research aim of granular computing approach for formal concept analysis is to incorporate these advantages of granular computing into traditional formal concept analysis for efficiently solving data analysis and processing. More specifically, this paper shows the main research topics of granular computing approach for formal concept analysis from the perspectives of Galois connection based granular computing model, object granule, attribute granule, relation granule, relation-based concept 山 东 大 学 学 报 (理 学 版)第52卷 - 第7期李金海,等:形式概念分析的粒计算方法及其研究展望 \=-granularity, granular rule, granular reduct, granular concept and learning, and concept granular computing systems. In addition, some challenging problems are also proposed for dealing with big data and cognitive learning. The obtained results will provide some references for the further study of granular computing approach of formal concept analysis.

Key words: formal concept analysis, concept lattice, cognitive learning, big data, granular computing

CLC Number: 

  • TP18
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