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On Characterizing Hierarchies of Granulation Structures via Distances

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Języki publikacji
EN
Abstrakty
EN
Hierarchy plays a crucial role in the development of the granular computing. In this paper, three different hierarchies are considered for judging whether a granulation structure is finer or coarser than another one. The first hierarchy is based on the set containment of information granulations, the second hierarchy is based on the cardinal numbers of information granulations while the third hierarchy is based on the sum of cardinal numbers of information granulations. Through introducing set distance and knowledge distance, we investigate the algebraic lattices, in which the derived partial orders are corresponding to the three different hierarchies, respectively. From the viewpoint of distance, these results look forward to provide a more comprehensible perspective for the study of hierarchies on granulation structures.
Wydawca
Rocznik
Strony
365--380
Opis fizyczny
Bibliogr. 27 poz.
Twórcy
autor
  • School of Comp. Sci. and Engineering, Jiangsu University of Sci. and Technology Zhenjiang, 212003, P.R. China
autor
  • Key Lab. of Comp. Intel. and Chinese, Information Processing of Ministry of Education Taiyuan, 030006, P.R. China
autor
  • School of Comp. Sci. and Technology, Nanjing University of Sci. and Technology Nanjing, 210093, P.R. China
Bibliografia
  • [1] Bargiela, A., Pedrycz, W.: Granular Computing: An Introduction, Kluwer Academic Publishers, Boston, 2002.
  • [2] Bargiela, A., Pedrycz, W.: Granular mappings, IEEE Transactions on Systems, Man and Cybernetics Part A, 35, 2005, 292–297.
  • [3] Chan, C. C., Tzeng, G. H.: Dominance–based rough sets using indexed blocks as granules, Fundamenta Informaticae, 94, 2009, 133–146.
  • [4] Liang, J. Y., Li, R., Qian Y. H.: Distance: A more comprehensible prespective for measures in rough set theory, Knowledge–Based Systems, 27, 2012, 126–136.
  • [5] Lin, T. Y.: Granular computing: practices, theories, and future directions, Encyclopedia on Complexity of Systems Science, 2009, 4339–4355.
  • [6] Lin, T. Y.: Granular computing I: the concept of granulation and its formal model, International Journal of Granular Computing, Rough Sets and Intelligent Systems, 1(1), 2009, 21–42.
  • [7] Marczewski E., Steinhaus H.: On a certain distance of sets and the corresponding distance of functions, Colloquium Mathematicum, 6, 1958, 319–327.
  • [8] Keet C. M.: From granulation hierarchy to granular perspective, 2009 IEEE International Conference on Granular Computing, 2009, 306–311.
  • [9] Pawlak, Z.: Rough sets–theoretical aspects of reasoning about data, Kluwer Academic Publishers, 1992.
  • [10] Pawlak, Z., Skowron, A.: Rudiments of rough sets, Information Sciences 177(1), 2007, 3–27.
  • [11] Pawlak, Z., Skowron, A.: Rough sets: Some extensions, Information Sciences, 177(1), 2007, 28–40.
  • [12] Pawlak, Z., Skowron, A.: Rough sets and boolean reasoning, Information Sciences, 177(1), 2007, 41–73.
  • [13] Qian, Y. H., Dang, C. Y., Liang, J. Y., Wu, W. Z.: Partial ordering of information granulations: a further investigation, Expert Systems, 29, 2012, 3–24.
  • [14] Qian, Y. H., Liang, J. Y., Dang, C. Y.: Knowledge structure, knowledge granulation and knowledge distance in a knowledge base, International Journal of Approximate Reasoning, 50, 2009, 174–188.
  • [15] Qian, Y. H., Liang, J. Y., Wu, W. Z., Dang, C. Y.: Information granularity in fuzzy binary GrC model, IEEE Transactions on Fuzzy Systems, 19, 2011, 253–264.
  • [16] Wang, G. Y., Zhang, Q. H.: Uncertainty of rough sets in different knowledge granularities, Chinese Journal of Computers, 2008, 31(9): 1588–1598.
  • [17] Wang, G. Y., Zhang, Q. H., Ma, X. A., Yang, Q. S.: Granular computing models for knowledge uncertainty, Chinese Journal of Software, 2011, 22(4): 676–694.
  • [18] Wu, W. Z., Leung, Y.: Theory and applications of granular labelled partitions in multi–scale decision tables, Information Sciences, 181, 2011, 3878–3897.
  • [19] Yao, Y. Y.: Information granulation and rough set approximation, International Journal of Intelligent Systems, 16, 2001, 87–104.
  • [20] Yao, Y. Y.: Perspectives of granular computing, 2005 IEEE International Conference on Granular Computing, 2005, 85–90.
  • [21] Yao, Y. Y.: Neighborhood systems and approximate retrieval, Information Sciences, 176, 2006, 3431–3452.
  • [22] Yao, Y. Y.: Granular computing: past, present and future, 2008 IEEE International Conference on Granular Computing, 2008, 80–85.
  • [23] Yao, Y. Y.: Triarchic theory of granular computing (in Chinese), Quotient Space Theory and Granular Computing, Theory and Practice of Structured Problem Solving (Zhang, Y. P., Luo, B. Yao, Y. Y., et al. Eds) Science Press, Beijing, 2010, 115–143.
  • [24] Yao, Y. Y., Zhang, N., Miao, D. Q., Xu, F. F.: Set–theoretic approaches to granular computing, Fundamenta Informaticae, 115, 2012, 247–264.
  • [25] Zadeh, L. A.: Fuzzy logic equals computing with words, IEEE Transactions on Fuzzy Systems, 4, 1996, 103–111.
  • [26] Zadeh, L. A.: Toward a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic, Fuzzy Sets and Systems, 90, 1997, 111–127.
  • [27] Zhang, L., Zhang, B.: The quotient space theory of problem solving, Fundamenta Informaticae, 59, 2004, 287–298.
Typ dokumentu
Bibliografia
Identyfikator YADDA
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