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

Journal of Intelligent Information Systems

Volumn 36, Issue 3, 2011, Pages 347-366

  Ou Yang, Yu Ping ^a   Shieh, How Ming ^a   Tzeng, Gwo Hshiung ^b,c   Yen, Leon ^b   Chan, Chien Chung ^b,d  

^a NATIONAL CENTRAL UNIVERSITY   (Taiwan)

^b KAINAN UNIVERSITY   (Taiwan)

^c NATIONAL CHIAO TUNG UNIVERSITY   (Taiwan)

^d The University of Akron   (United States)

Author keywords

Business aviation; Data mining; Decision making; Decision rule; Formal concept analysis; Information system; Rough set theory

Indexed keywords

BUSINESS AVIATION; DECISION RULES; DEREGULATED MARKETS; EAST ASIA; EMPIRICAL STUDIES; FLOW GRAPH; FORMAL CONCEPT ANALYSIS; ROUGH SET; SOUTH AMERICA;

AVIATION; DECISION MAKING; DEREGULATION; INFORMATION ANALYSIS;

ROUGH SET THEORY;

EID: 79958865000     PISSN: 09259902     EISSN: 15737675     Source Type: Journal    
DOI: 10.1007/s10844-009-0110-y     Document Type: Article

References (46)

1. R Belohlavek 1999 Fuzzy Galois connections Mathematical Logic Quarterly 45 497 504 1724419 0938.03079 10.1002/malq.19990450408
2. R Belohlavek 2004 Concept lattices and order in fuzzy logic Annals of Pure and Applied Logic 128 1-3 277 298 2060556 1060.03040 10.1016/j.apal.2003. 01.001 (Pubitemid 38613209)
3. Berthold, M.; & Hand, D. J. (1999). Intelligent data analysis-An introduction. Berlin: Springer.
4. MJ Beynon MJ Peel 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 10.1016/S0305-0483(01) 00045-7 (Pubitemid 33628771)
5. C Carpineto G Romano 1996 A lattice conceptual clustering system and its application to browsing retrieval Machine Learning 24 95 122 (Pubitemid 126724381)
6. CC Chan 1998 A rough set approach to attribute generalization in data mining Journal of Information Sciences 107 1-4 169 176 10.1016/S0020-0255(97) 10047-0
7. 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.
8. B Diaz-Agudo PA Gonzalez-Calero 2001 Formal concept analysis as a support technique for CBR Knowledge-Based Systems 14 3-4 163 171 10.1016/S0950-7051(01) 00093-4 (Pubitemid 32491731)
9. AI Dimitras R Slowinski R Susmaga C Zopounidis 1999 Business failure prediction using rough sets European Journal of Operational Research 114 2 263 280 0963.91023 10.1016/S0377-2217(98)00255-0
10. 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.
11. Ford, L. R.; & Fulkerson, D. R. (1962). Flows in networks. Princeton: Princeton University Press.
12. R Godin R Missaoi 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 1300702 0938.68806 10.1016/0304-3975(94)90195-3
13. 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.
14. SK Harms JS Deogum 2004 Sequential association rule mining with time lags Journal of Intelligent Information Systems 22 1 7 22 10.1023/A:1025824629047
15. YC Hu RS Chen GH Tzeng 2003 Finding fuzzy classification rules using data mining techniques Pattern Recognition Letters 24 1-3 509 519 1053.68084 10.1016/S0167-8655(02)00273-8 (Pubitemid 36080792)
16. E Krusinska R Slowinski J Stefanowski 1992 Discriminant versus rough set approach to vague data analysis Applied Stochastic Models and Data Analysis 8 1 43 56 0800.62006 10.1002/asm.3150080107
17. R Li ZO Wang 2004 Mining classification rules using rough sets and neural networks European Journal of Operational Research 157 2 439 448 1106.68386 10.1016/S0377-2217(03)00422-3
18. G Mineau R Godin 1995 Automatic structuring of knowledge bases by conceptual clustering IEEE Transactions on Knowledge and Data Engineering 7 5 824 829 10.1109/69.469834
19. NBAA Fact Book (2004). National Business Aviation Association, INC. http://www.gaservingamerica.org/library-pdfs/BUSINE-1.PDF.
20. Z Pawlak 1982 Rough sets International Journal of Computer and Information Sciences 11 5 341 356 703291 0501.68053 10.1007/BF01001956
21. Pawlak, Z. (1991). Rough sets: Theoretical aspects of reasoning about data. Dordrecht: Kluwer.
22. Pawlak, Z. (1997). Rough sets. In T. Y. Lin, & N. Cercone (Eds.), Rough sets and data mining (pp. 3-8). Dordrecht: Kluwer.
23. Z Pawlak 2002 Rough sets, decision algorithms and Bayes' theorem European Journal of Operational Research 136 1 181 189 1863671 1089.68127 10.1016/S0377-2217(01)00029-7 (Pubitemid 33018811)
24. Z Pawlak 2005 Flow graphs and intelligent data analysis Fundamenta Informaticae 64 1/4 369 377 2347567 1102.68118 (Pubitemid 41218142)
25. Z Pawlak A Skowron 2007 Rudiments of rough sets Information Sciences 177 1 3 27 2272732 1142.68549 10.1016/j.ins.2006.06.003 (Pubitemid 44556698)
26. 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.
27. 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.
28. 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. (Pubitemid 128093468)
29. J Saquer J Deogun 2003 Approximating monotone concepts Design and Application of Hybrid Intelligent System 105 605 613
30. M Shao M Liu W Zhang 2007 Set approximations in fuzzy formal concept analysis Fuzzy Sets and Systems 158 23 2627 2640 2363784 1127.68104 10.1016/j.fss.2007.05.002 (Pubitemid 47445750)
31. L Shen EH Tay L Qu Y Shen 2000 Fault diagnosis using rough sets theory Computers in Industry 43 1 61 72 10.1016/S0166-3615(00)00050-6
32. 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.
33. Slowinski, R. (Ed.) (1992). Intelligent decision support-Handbook of applications and advances of the rough sets theory. Dordrecht: Kluwer.
34. R Slowinski C Zopounidis 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
35. 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.
36. B Walczak DL Massart 1999 Rough sets theory Chemometrics and Intelligent Laboratory Systems 47 1 1 16 10.1016/S0169-7439(98)00200-7 (Pubitemid 29100094)
37. L Wang X Liu 2008 A new model of evaluating concept similarity Knowledge-Based Systems 21 842-486 10.1016/j.knosys.2008.03.042
38. E Weiss 1997 Rough sets, rough neurons, induction and data mining #2 Journal of Computational Intelligence in Finance 5 3 10 11
39. Wille, R. (1982). Restructuring lattice theory: An approach based on hierarchies of concepts. In I. Rival (Ed.), Ordered sets (pp. 445-470). Dordrecht: Reidel.
40. 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.
41. 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.
42. F Witlox H Tindemans 2004 The application of rough sets analysis in activity-based modeling, opportunities and constraints Expert Systems with Application 27 2 171 180
43. 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).
44. LY Zhai LP Khoo SC Fok 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 10.1016/S0360-8352(02)00131-6 (Pubitemid 34991782)
45. W Zhang J Ma S Fan 2007 Variable threshold concept lattices Information Sciences 177 22 4883 4892 2362800 1130.06004 10.1016/j.ins.2007.05.031 (Pubitemid 47331455)
46. Y Zhao WA Halang X Wang 2007 Rough ontology mapping in E-business integration Studies in Computational Intelligence 37 75 93 10.1007/978-3-540- 37017-8-3