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Rough Set Approximations in Formal Concept Analysis

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Transactions on Rough Sets V

Part of the book series: Lecture Notes in Computer Science ((TRS,volume 4100))

Abstract

A basic notion shared by rough set analysis and formal concept analysis is the definability of a set of objects based on a set of properties. The two theories can be compared, combined and applied to each other based on definability. In this paper, the notion of rough set approximations is introduced into formal concept analysis. Rough set approximations are defined by using a system of definable sets. The similar idea can be used in formal concept analysis. The families of the sets of objects and the sets of properties established in formal concept analysis are viewed as two systems of definable sets. The approximation operators are then formulated with respect to the systems. Two types of approximation operators, with respect to lattice-theoretic and set-theoretic interpretations, are studied. The results provide a better understanding of data analysis using rough set analysis and formal concept analysis.

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References

  1. Birkhoff, G.: Lattice Theory, 3rd edn. American Mathematical Society Colloquium Publications, Providence (1967)

    MATH  Google Scholar 

  2. Buszkowski, W.: Approximation spaces and definability for incomplete information systems. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS, vol. 1424, pp. 115–122. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  3. Cohn, P.M.: Universal Algebra. Harper and Row Publishers, New York (1965)

    MATH  Google Scholar 

  4. Düntsch, I., Gediga, G.: Approximation operators in qualitative data analysis. In: de Swart, H., Orłowska, E., Schmidt, G., Roubens, M. (eds.) Theory and Applications of Relational Structures as Knowledge Instruments. LNCS, vol. 2929, pp. 214–230. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  5. Gediga, G., Düntsch, I.: Modal-style operators in qualitative data analysis. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 155–162 (2002)

    Google Scholar 

  6. Ganter, B., Wille, R.: Formal Concept Analysis: Mathematical Foundations. Springer, New York (1999)

    MATH  Google Scholar 

  7. Ho, T.B.: Acquiring concept approximations in the framework of rough concept analysis. In: Proceedings of 7th European-Japanese Conference on Information Modelling and Knowledge Bases, pp. 186–195 (1997)

    Google Scholar 

  8. Hu, K., Sui, Y., Lu, Y.-c., Wang, J., Shi, C.-Y.: Concept approximation in concept lattice. In: Cheung, D., Williams, G.J., Li, Q. (eds.) PAKDD 2001. LNCS, vol. 2035, pp. 167–173. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  9. Kent, R.E.: Rough concept analysis. Fundamenta Informaticae 27, 169–181 (1996)

    MATH  MathSciNet  Google Scholar 

  10. Pagliani, P.: From concept lattices to approximation spaces: algebraic structures of some spaces of partial objects. Fundamenta Informaticae 18, 1–25 (1993)

    MATH  MathSciNet  Google Scholar 

  11. Pagliani, P., Chakraborty, M.K.: Information quanta and approximation spaces. I: non-classical approximation operators. In: Proceedings of IEEE International Conference on Granular Computing, pp. 605–610 (2005)

    Google Scholar 

  12. Pawlak, Z.: Rough sets. International Journal of Computer and Information Sciences 11, 341–356 (1982)

    Article  MATH  MathSciNet  Google Scholar 

  13. Pawlak, Z.: Rough Sets - Theoretical Aspects of Reasoning About Data. Kluwer Publishers, Dordrecht (1991)

    MATH  Google Scholar 

  14. Pei, D.W., Xu, Z.B.: Rough set models on two universes. International Journal of General Systems 33, 569–581 (2004)

    Article  MATH  MathSciNet  Google Scholar 

  15. Qi, J.-J., Wei, L., Li, Z.-z.: A partitional view of concept lattice. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 74–83. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  16. Saquer, J., Deogun, J.S.: Formal rough concept analysis. In: Zhong, N., Skowron, A., Ohsuga, S. (eds.) RSFDGrC 1999. LNCS, vol. 1711, pp. 91–99. Springer, Heidelberg (1999)

    Chapter  Google Scholar 

  17. Saquer, J., Deogun, J.: Concept approximations based on rough sets and similarity measures. International Journal of Applied Mathematics and Computer Science 11, 655–674 (2001)

    MATH  MathSciNet  Google Scholar 

  18. Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)

    MATH  Google Scholar 

  19. Shafer, G.: Belief functions and possibility measures. In: Bezdek, J.C. (ed.) Analysis of Fuzzy information, mathematics and logic, vol. 1, pp. 51–84. CRC Press, Boca Raton (1987)

    Google Scholar 

  20. Shao, M.-W., Zhang, W.-x.: Approximation in formal concept analysis. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 43–53. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  21. Wasilewski, P.: Concept lattices vs. Approximation spaces. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 114–123. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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

    Google Scholar 

  23. Wolski, M.: Galois connections and data analysis. Fundamenta Informaticae CSP, 1–15 (2003)

    Google Scholar 

  24. Wolski, M.: Formal concept analysis and rough set theory from the perspective of finite topological approximations. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 230–243. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  25. Wong, S.K.M., Wang, L.S., Yao, Y.Y.: Interval structure: a framework for representing uncertain information. In: Uncertainty in Artificial Intelligence: Proceedings of the 8th Conference, pp. 336–343 (1993)

    Google Scholar 

  26. Wong, S.K.M., Wang, L.S., Yao, Y.Y.: On modeling uncertainty with interval structures. Computational Intelligence 11, 406–426 (1995)

    Article  MathSciNet  Google Scholar 

  27. Wu, Q., Liu, Z.T., Li, Y.: Rough formal concepts and their accuracies. In: Proceedings of the 2004 International Conference on Services Computing, SCC 2004, pp. 445–448 (2004)

    Google Scholar 

  28. Yao, Y.Y.: Two views of the theory of rough sets in finite universe. International Journal of Approximate Reasoning 15, 291–317 (1996)

    Article  MATH  MathSciNet  Google Scholar 

  29. Yao, Y.Y.: Generalized rough set models. In: Polkowski, L., Skowron, A. (eds.) Rough Sets in Knowledge Discovery, pp. 286–318. Physica-Verlag, Heidelberg (1998)

    Google Scholar 

  30. Yao, Y.Y.: On Generalizing Pawlak Approximation Operators. In: Polkowski, L., Skowron, A. (eds.) RSCTC 1998. LNCS, vol. 1424, pp. 298–307. Springer, Heidelberg (1998)

    Chapter  Google Scholar 

  31. Yao, Y.Y.: Constructive and algebraic methods of the theory of rough sets. Information Sciences 109, 21–47 (1998)

    Article  MATH  MathSciNet  Google Scholar 

  32. Yao, Y.Y.: On generalizing rough set theory. In: Wang, G., Liu, Q., Yao, Y., Skowron, A. (eds.) RSFDGrC 2003. LNCS, vol. 2639, pp. 44–51. Springer, Heidelberg (2003)

    Chapter  Google Scholar 

  33. Yao, Y.: A comparative study of formal concept analysis and rough set theory in data analysis. In: Tsumoto, S., Słowiński, R., Komorowski, J., Grzymała-Busse, J.W. (eds.) RSCTC 2004. LNCS, vol. 3066, pp. 59–68. Springer, Heidelberg (2004)

    Chapter  Google Scholar 

  34. Yao, Y.Y.: Concept lattices in rough set theory. In: Proceedings of 23rd International Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2004, pp. 796–801 (2004)

    Google Scholar 

  35. Yao, Y.Y., Chen, Y.H.: Rough set approximations in formal concept analysis. In: Proceedings of 23rd International Meeting of the North American Fuzzy Information Processing Society, NAFIPS 2004, pp. 73–78 (2004)

    Google Scholar 

  36. Yao, Y., Chen, Y.: Subsystem based generalizations of rough set approximations. In: Hacid, M.-S., Murray, N.V., Raś, Z.W., Tsumoto, S. (eds.) ISMIS 2005. LNCS, vol. 3488, pp. 210–218. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

  37. Yao, Y.Y., Lin, T.Y.: Generalization of rough sets using modal logic. Intelligent Automation and Soft Computing, An International Journal 2, 103–120 (1996)

    Google Scholar 

  38. Zadeh, L.A.: Fuzzy logic as a basis for a theory of hierarchical definability (THD). In: Proceedings of the 33rd International Symposium on Multiple-Valued Logic, ISMVL 2003, pp. 3–4 (2003)

    Google Scholar 

  39. Zhang, W.X., Wei, L., Qi, J.J.: Attribute reduction in concept lattice based on discernibility matrix. In: Ślęzak, D., Wang, G., Szczuka, M.S., Düntsch, I., Yao, Y. (eds.) RSFDGrC 2005. LNCS, vol. 3641, pp. 157–165. Springer, Heidelberg (2005)

    Chapter  Google Scholar 

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Yao, Y., Chen, Y. (2006). Rough Set Approximations in Formal Concept Analysis. In: Peters, J.F., Skowron, A. (eds) Transactions on Rough Sets V. Lecture Notes in Computer Science, vol 4100. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11847465_14

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  • DOI: https://doi.org/10.1007/11847465_14

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-39382-5

  • Online ISBN: 978-3-540-39383-2

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