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Combined rough sets with flow graph and formal concept analysis for business aviation decision-making

Journal of Intelligent Information Systems volume 36, pages 347–366 (2011)Cite this article

Abstract

Although business aviation has been popular in the USA, Europe, and South America, however, top economies in East Asia, including Japan, Korea, and Taiwan, have been more conservative and lag behind in the development of business aviation. In this paper, we hope to discover possible trends and needs of business aviation for supporting the government to make decision in anticipation of eventual deregulation in the near future. We adopt knowledge-discovery tools based on rough set to analyze the potential for business aviation through an empirical study. Although our empirical study uses data from Taiwan, we are optimistic that our proposed method can be similarly applied in other countries to help governments there make decisions about a deregulated market in the future.

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References

  • Belohlavek, R. (1999). Fuzzy Galois connections. Mathematical Logic Quarterly, 45, 497–504.

    Article  MathSciNet  MATH  Google Scholar 

  • Belohlavek, R. (2004). Concept lattices and order in fuzzy logic. Annals of Pure and Applied Logic, 128(1–3), 277–298.

    Article  MathSciNet  MATH  Google Scholar 

  • Berthold, M., & Hand, D. J. (1999). Intelligent data analysis—An introduction. Berlin: Springer.

    MATH  Google Scholar 

  • Beynon, M. J., & Peel, M. J. (2001). Variable precision rough set theory and data discretisation: An application to corporate failure prediction. Omega, International Journal of Management Science, 29(6), 561–576.

    Article  Google Scholar 

  • Carpineto, C., & Romano, G. (1996). A lattice conceptual clustering system and its application to browsing retrieval. Machine Learning, 24, 95–122.

    Google Scholar 

  • Chan, C. C. (1998). A rough set approach to attribute generalization in data mining. Journal of Information Sciences, 107(1–4), 169–176.

    Article  Google Scholar 

  • Chan, C. C., & Santhosh, S. (2003). BLEM2: Learning Bayes’ rules from examples using rough sets. In Proc. NAFIPS 2003, 22nd int. conf. of the North American fuzzy information processing society, 24–26 July 2003, Chicago, Illinois, pp. 187–190.

  • Diaz-Agudo, B., & Gonzalez-Calero, P. A. (2001). Formal concept analysis as a support technique for CBR. Knowledge-Based Systems, 14(3–4), 163–171.

    Article  Google Scholar 

  • Dimitras, A. I., Slowinski, R., Susmaga, R., & Zopounidis, C. (1999). Business failure prediction using rough sets. European Journal of Operational Research, 114(2), 263–280.

    Article  MATH  Google Scholar 

  • Dubois, D., & Prade, H. (1992). Putting rough sets and fuzzy sets together. In R. Slowinski (Ed.), Intelligent decision support: Handbook of applications and advances of the rough sets theory (pp. 203–232). Dordrecht: Kluwer.

    Google Scholar 

  • Ford, L. R., & Fulkerson, D. R. (1962). Flows in networks. Princeton: Princeton University Press.

    MATH  Google Scholar 

  • Godin, R., & Missaoi, R. (1994). An incremental concept formation approach for learning from databases, in: Formal methods in databases and software engineering. Theoretical Computer Science, 133(special issue), 387–419.

    Article  MathSciNet  MATH  Google Scholar 

  • Greco, S., Matarazzo, B., & Slowinski, R. (1998). A new rough set approach to evaluation of bankruptcy risk. In C. Zopounidis (Ed.), Operational tools in the management of financial risks (pp. 121–136). Dordrecht: Kluwer.

    Google Scholar 

  • Harms, S. K., & Deogum, J. S. (2004). Sequential association rule mining with time lags. Journal of Intelligent Information Systems, 22(1), 7–22.

    Article  Google Scholar 

  • Hu, Y. C., Chen, R. S., & Tzeng, G. H. (2003). Finding fuzzy classification rules using data mining techniques. Pattern Recognition Letters, 24(1–3), 509–519.

    Article  MATH  Google Scholar 

  • Krusinska, E., Slowinski, R., & Stefanowski, J. (1992). Discriminant versus rough set approach to vague data analysis. Applied Stochastic Models and Data Analysis, 8(1), 43–56.

    Article  MATH  Google Scholar 

  • Li, R., & Wang, Z. O. (2004). Mining classification rules using rough sets and neural networks. European Journal of Operational Research, 157(2), 439–448.

    Article  MATH  Google Scholar 

  • Mineau, G., & Godin, R. (1995). Automatic structuring of knowledge bases by conceptual clustering. IEEE Transactions on Knowledge and Data Engineering, 7(5), 824–829.

    Article  Google Scholar 

  • NBAA Fact Book (2004). National Business Aviation Association, INC. http://www.gaservingamerica.org/library_pdfs/BUSINE_1.PDF.

  • Pawlak, Z. (1982). Rough sets. International Journal of Computer and Information Sciences, 11(5), 341–356.

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer.

    MATH  Google Scholar 

  • Pawlak, Z. (1997). Rough sets. In T. Y. Lin, & N. Cercone (Eds.), Rough sets and data mining (pp. 3–8). Dordrecht: Kluwer.

    Google Scholar 

  • Pawlak, Z. (2002). Rough sets, decision algorithms and Bayes’ theorem. European Journal of Operational Research, 136(1), 181–189.

    Article  MathSciNet  MATH  Google Scholar 

  • Pawlak, Z. (2005). Flow graphs and intelligent data analysis. Fundamenta Informaticae, 64(1/4), 369–377.

    MathSciNet  MATH  Google Scholar 

  • Pawlak, Z., & Skowron, A. (2007). Rudiments of rough sets. Information Sciences, 177(1), 3–27.

    Article  MathSciNet  MATH  Google Scholar 

  • Polkowski, L., & Skowron, A. (Eds.) (1998). Rough sets in knowledge discovery 1: Approach and applications; rough sets in knowledge discovery 2: Applications, case studies, and software systems. Wurzburg: Physica.

    Google Scholar 

  • Predki, B., & Wilk, S. Z. (1999). Rough set based data exploration using ROSE system. In Z. W. Ras, & A. Skowron (Eds.), Foundations of intelligent systems, lecture notes in artificial intelligence (Vol. 1609, pp. 172–180). Berlin: Springer.

    Google Scholar 

  • Predki, B., Slowinski, R., Stefanowski, J., Susmaga, R., & Wilk, S. Z. (1998). ROSE—Software implementation of the rough set theory. In L. Polkowski, & A. Skowron (Eds.), Rough sets and current trends in computing, lecture notes in artificial intelligence (Vol. 1424, pp. 605–608). Berlin: Springer.

  • Saquer, J., & Deogun, J. (2003). Approximating monotone concepts. Design and Application of Hybrid Intelligent System, 105, 605–613.

    Google Scholar 

  • Shao, M., Liu, M., & Zhang, W. (2007). Set approximations in fuzzy formal concept analysis. Fuzzy Sets and Systems, 158(23), 2627–2640.

    Article  MathSciNet  MATH  Google Scholar 

  • Shen, L., Tay, E. H., Qu, L., & Shen, Y. (2000). Fault diagnosis using rough sets theory. Computers in Industry, 43(1), 61–72.

    Article  Google Scholar 

  • Skowron, A., & Grzymala-Busse, J. W. (1993). From the rough set theory to the evidence theory. In M. Fedrizzi, J. Kacprzyk, & R. R. Yager (Eds.), Advances in the Dempster–Shafer theory of evidence (pp. 295–305). New York: Wiley.

    Google Scholar 

  • Slowinski, R. (Ed.) (1992). Intelligent decision support—Handbook of applications and advances of the rough sets theory. Dordrecht: Kluwer.

    MATH  Google Scholar 

  • Slowinski, R., & Zopounidis, C. (1995). Application of the rough set approach to evaluation of bankruptcy risk. International Journal of Intelligent Systems in accounting, Finance and Management, 4(1), 27–41.

    Google Scholar 

  • Tsumoto, S., Kobayashi, S., Tanaka, H., & Nakamura, A. (Eds.) (1996). Proceedings of the fourth international workshop on rough sets, fuzzy sets and machine discovery, RSDF’96. Tokyo.

  • Walczak, B., & Massart, D. L. (1999). Rough sets theory. Chemometrics and Intelligent Laboratory Systems, 47(1), 1–16.

    Article  Google Scholar 

  • Wang, L., & Liu, X. (2008). A new model of evaluating concept similarity. Knowledge-Based Systems, 21, 842–486. doi:10.1016/j.knosys.2008.03.042.

    Google Scholar 

  • Weiss, E. (1997). Rough sets, rough neurons, induction and data mining #2. Journal of Computational Intelligence in Finance, 5(3), 10–11.

    Google Scholar 

  • Wille, R. (1982). Restructuring lattice theory: An approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered sets (pp. 445–470). Dordrecht: Reidel.

    Google Scholar 

  • Wille, R. (1989). Knowledge acquisition by methods of formal concept analysis. Knowledge acquisition by methods of formal concept analysis (pp. 365–380). New York: Nova Science.

    Google Scholar 

  • Wille, R. (2005). Formal concept analysis as methodical theory of concepts and concept hierarchies. In B. Granter et al. (Eds.), Formal concept analysis, LNAI 3626 (pp. 1–3). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Witlox, F., & Tindemans, H. (2004). The application of rough sets analysis in activity-based modeling, opportunities and constraints. Expert Systems with Application, 27(2), 171–180.

    Google Scholar 

  • Yao, Y. (2004). Concept lattices in rough set theory. In Proceedings of 2004 annual meeting of the north american fuzzy information processing society 2 (pp. 796–801).

  • Zhai, L. Y., Khoo, L. P., & Fok, S. C. (2002). Feature extraction using rough set theory and genetic algorithms an application for the simplification of product quality evaluation. Computers & Industrial Engineering, 43(4), 661–676.

    Article  Google Scholar 

  • Zhang, W., Ma, J., & Fan, S. (2007). Variable threshold concept lattices. Information Sciences, 177(22), 4883–4892.

    Article  MathSciNet  MATH  Google Scholar 

  • Zhao, Y., Halang, W. A., & Wang, X. (2007). Rough ontology mapping in E-business integration. Studies in Computational Intelligence, 37, 75–93.

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Department of Business Administration, National Central University, 300 Chung-da Road, Chung-Li City, 320, Taiwan

    Yu-Ping Ou Yang & How-Ming Shieh

  2. Department of Business and Entrepreneurial Management, Kainan University, No. 1, Kainan Road, Luzhu, Taoyuan, 338, Taiwan

    Gwo-Hshiung Tzeng

  3. Institute of Management of Technology, National Chiao Tung University, 1001 Ta-Hsueh Road, Hsinchu, 300, Taiwan

    Gwo-Hshiung Tzeng

  4. Department of International Business, Kainan University, Taoyuan County, Taiwan

    Leon Yen

  5. Department of Computer Science, University of Akron, Akron, OH, 44325, USA

    Chien-Chung Chan

  6. Department of Information Communications, Kainan University, Luzhu, Taoyuan County, Taiwan

    Chien-Chung Chan

Authors
  1. Yu-Ping Ou Yang
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  2. How-Ming Shieh
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  3. Gwo-Hshiung Tzeng
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  4. Leon Yen
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  5. Chien-Chung Chan
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Corresponding author

Correspondence to Chien-Chung Chan.

Appendices

Appendix 1

Table 9 Attribute specifications in the information table (the first stage)
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Table 10 Attribute specifications in the information table (the second stage)
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Appendix 2

Table 11 Rules for decision attribute D1, the possibility of utilizing business aviation (for the reduct Set1)
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Ou Yang, YP., Shieh, HM., Tzeng, GH. et al. Combined rough sets with flow graph and formal concept analysis for business aviation decision-making. J Intell Inf Syst 36, 347–366 (2011). https://doi.org/10.1007/s10844-009-0110-y

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  • DOI: https://doi.org/10.1007/s10844-009-0110-y

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