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Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing pp 286–297Cite as

A Knowledge Acquisition Model Based on Formal Concept Analysis in Complex Information Systems

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9437))

Abstract

Normally, in some complex information systems, the binary relation on domain of any attribute is just a kind of ordinary binary, which does not meet some common properties such as reflexivity, transitivity or symmetry. In view of the above-mentioned facts this paper attempts to employ FCA(Formal Concept Analysis), proposes a rough set model based on FCA, in which equivalence relations, dominance relations, similarity relations(or tolerance relations) and neighborhood relations on universe are expanded to general binary relations and problems in rough set theory are discussed based on FCA. Particularly, from the above description of complex information systems, we can see that the relation in domain of any attribute may be extremely complex, which often leads to high time complexity and space complexity in the process of knowledge acquisition. For above reason this paper introduces granular computing(GrC), which can effectively reduce the complexity to a certain extent.

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References

  1. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, Berlin (1999)

    Book  MATH  Google Scholar 

  2. Greco, S., Matarazzo, B., Slowinski, R.: Rough approximation of a preference relation by dominance relation. Eur. J. Oper. Res. 117, 63–83 (1999)

    Article  MATH  Google Scholar 

  3. Kang, X.P., Li, D.Y., Wang, S.G., Qu, K.S.: Rough set model based on formal concept analysis. Inf. Sci. 222, 611–625 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  4. Leung, Y., Li, D.Y.: Maximal consistent block technique for rule acquisition in incomplete information systems. Inf. Sci. 153, 85–106 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  5. Mi, J.S., Leung, Y., Wu, W.Z.: Approaches to attribute reduct in concept lattices induced by axialities. Knowl.-Based Syst. 23, 504–511 (2010)

    Article  Google Scholar 

  6. Pawlak, Z.: Rough sets. Int. J. Comput. Inform. Sci. 11, 341–356 (1982)

    Article  MATH  Google Scholar 

  7. Saaty, T.L.: A scaling method for priorities in hierarchical structures. J. Math. Psychol. 15(3), 234–281 (1977)

    Article  MathSciNet  MATH  Google Scholar 

  8. Slowinski, R., Vanderpooten, D.: A generalized definition of rough approximations based on similarity. Knowl. Data Eng. 12, 331–336 (2000)

    Article  Google Scholar 

  9. Wei, L., Qi, J.J.: Relation between concept lattice reduct and rough set reduct. Knowl.-Based Syst. 23, 934–938 (2010)

    Article  Google Scholar 

  10. Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival, I. (ed.) Ordered Sets, pp. 445–470. Reidel, Dordrecht, Boston (1982)

    Chapter  Google Scholar 

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Acknowledgments

We would like to thank anonymous reviewers very much for their professional comments and valuable suggestions. This work was supported by the National Postdoctoral Science Foundation of China (No. 2014M560352) and the National Natural Science Foundation of China (No. 61273304).

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Authors and Affiliations

  1. The Key Laboratory of Embedded System and Service Computing, Ministry of Education, Tongji University, Shanghai, 200092, People’s Republic of China

    Xiangping Kang & Duoqian Miao

  2. Department of Information Science and Technology, East China University of Political Science and Law, Shanghai, 201620, People’s Republic of China

    Na Jiao

Authors
  1. Xiangping Kang
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  2. Duoqian Miao
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  3. Na Jiao
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Corresponding author

Correspondence to Xiangping Kang .

Editor information

Editors and Affiliations

  1. University of Regina, Regina, SK, Canada

    Yiyu Yao

  2. Tianjin University, Tianjin, China

    Qinghua Hu

  3. Chongqing University of Posts and Telecommunications, Chongqing, China

    Hong Yu

  4. University of Kansas, Lawrence, KS, USA

    Jerzy W. Grzymala-Busse

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Kang, X., Miao, D., Jiao, N. (2015). A Knowledge Acquisition Model Based on Formal Concept Analysis in Complex Information Systems. In: Yao, Y., Hu, Q., Yu, H., Grzymala-Busse, J.W. (eds) Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing. Lecture Notes in Computer Science(), vol 9437. Springer, Cham. https://doi.org/10.1007/978-3-319-25783-9_26

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  • DOI: https://doi.org/10.1007/978-3-319-25783-9_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-25782-2

  • Online ISBN: 978-3-319-25783-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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