Abstract
Flow graph (FG) is a new mathematical model which can be used for representing, analyzing, and discovering knowledge in databases. Due to its well-structured characteristics of network, FG is naturally consistent with granular computing (GrC). Meanwhile, GrC provides us with both structured thinking at the philosophical level and structured problem solving at the practical level. In this paper, the relationship between FG and GrC will be discussed from three aspects under GrC at first, and then inference and reformation in FG can be easily implemented in virtue of decomposition and composition of granules, respectively. As a result of inference and reformation, the reformed FG is a reduction of the original one.
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References
Butz, C.J., Yan, W., Yang, B.: The Computational Complexity of Inference using Rough Set Flow Graphs. In: [15], pp. 335–344 (2005)
Butz, C.J., Yan, W., Yang, B.: An Efficient Algorithm for Inference in Rough Set Flow Graphs. Transaction on Rough Sets V 5, 102–122 (2006)
Czyzewski, A., Szczerba, M., Kostek, B.: Musical Metadata Retrieval with Flow Graphs. In: [18], pp. 691–698 (2004)
Kostek, B., Czyzewski, A.: Processing of Musical Metadata Employing Pawlak’s Flow Graphs. In: [14], pp. 279–298 (2004)
Lin, T.Y.: Granular Computing on Binary Relations I: Data Mining and Neighborhood Systems. In: Skoworn, A., Polkowski, L. (eds.) Rough Sets In Knowledge Discovery, pp. 107–121. Springer, Heidelberg (1998)
Lin, T.Y., Yin, P.: Heuristically Fast Finding of the Shortest Reducts. In: [18], pp. 465–470 (2004)
Pawlak, Z.: Decision algorithms, Bayes Theorem and Flow Graphs, In: Proceeding of the 6th International Conference on Neural Networks & Soft Computing (2002)
Pawlak, Z.: Flow graphs and decision algorithms. In: [19], pp. 1–11(2003)
Pawlak, Z.: Decision Networks. In: [18], pp. 1–7 (2004)
Pawlak, Z.: Some Issues on Rough Sets. In: [14], pp. 1–58 (2004)
Pawlak, Z.: Decisions rules and flow networks. European Journal of Operational Research 154, 184–190 (2004)
Pawlak, Z.: Rough Sets and Flow Graphs. In: [15], pp. 1–11 (2005)
Pawlak, Z.: Flow Graphs and Data Mining. In: Peters, J.F., Skowron, A. (eds.) Transactions on Rough Sets III. LNCS, vol. 3400, pp. 1–58. Springer, Heidelberg (2005)
Peters, J.F., Skowron, A., Grzymała-Busse, J.W., Kostek, B, Świniarski, R.W., Szczuka, M. (eds.): Transactions on Rough Sets I. LNCS, vol. 3100. Springer, Heidelberg (2004)
Ślȩzak, D., et al. (eds.): Rough Sets, Fuzzy Sets, Data Mining and Granular Computing. Springer, Berlin (2005)
Sun, J., Liu, H., Zhang, H.: An Extension of Pawlak’s Flow Graphs. In: Wang, G.-Y., Peters, J.F., Skowron, A., Yao, Y. (eds.) RSKT 2006. LNCS (LNAI), vol. 4062, pp. 191–199. Springer, Heidelberg (2006)
Sun, J., Liu, H., Qi, C., Zhang, H.: An Interpretation of Flow Graphs by Granular Computing. In: Greco, S., Hata, Y., Hirano, S., Inuiguchi, M., Miyamoto, S., Nguyen, H.S., Słowiński, R. (eds.) RSCTC 2006. LNCS (LNAI), vol. 4259, pp. 448–457. Springer, Heidelberg (2006)
Tsumoto, S., Słowiński, R., Komorowski, J. (eds.): RSCTC 2004. LNCS (LNAI), vol. 3066. Springer, Heidelberg (2004)
Wang, G.Y., et al. (eds.): Rough Sets,Fuzzy Sets,Data Mining and Granular Computing. FSRSGrC 2005. Springer, Heidelberg (2003)
Yao, Y.Y.: A partition model of granular computing. In: [14], pp. 232–253 (2004)
Yao, Y.Y.: Perspectives of Granular Computing. In: Proceedings of 2005 IEEE International Conference on Granular Computing, vol. 1, pp. 85–90. IEEE Computer Society Press, Los Alamitos (2005)
Yao, Y.Y., Zhong, N.: Granular computing using information tables. In: Lin, T.Y., Yao, Y.Y., Zadeh, L.A. (eds.) Data Mining, Rough Sets and Granular Computing, pp. 102–124. Physica-Verlag, Heidelberg (2002)
Zhang, L., Zhang, B.: The quotient space theory of problem solving, In: [19], pp. 11–15 (2003)
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Liu, H., Sun, J., Qi, C., Bai, X. (2007). Inference and Reformation in Flow Graphs Using Granular Computing. In: Kryszkiewicz, M., Peters, J.F., Rybinski, H., Skowron, A. (eds) Rough Sets and Intelligent Systems Paradigms. RSEISP 2007. Lecture Notes in Computer Science(), vol 4585. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-73451-2_28
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DOI: https://doi.org/10.1007/978-3-540-73451-2_28
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