Attribute reduction theory of concept lattice based on decision formal contexts

Science in China, Series F: Information Sciences

Volumn 51, Issue 7, 2008, Pages 910-923

  Wei, Ling ^a   Qi, Jianjun ^b   Zhang, Wenxiu ^c  

^a NORTHWEST UNIVERSITY   (China)

^b XIDIAN UNIVERSITY   (China)

^c XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Attribute reduction; Concept lattice; Decision formal context; Discernibility matrix; Implication mapping

Indexed keywords

EID: 44949215958     PISSN: 10092757     EISSN: 18622836     Source Type: Journal    
DOI: 10.1007/s11432-008-0067-4     Document Type: Article

References (18)

1. Wille R. Restructuring lattice theory: An approach based on hierarchies of concepts. In: Rival I, ed. Ordered Sets. Reidel: Dordrecht-Boston, 1982. 445-470
2. Oosthuizen G D. The application of concept lattice to machine learning. Technical Report. University of Pretoria, South Africa, 1996
3. Ho T B. Incremental conceptual clustering in the framework of Galois lattice. In: Lu H, Motoda H, Liu H, eds. KDD: Techniques and Applications. Singapore: World Scientific, 1997. 49-64
4. Kent R E, Bowman C M. Digital libraries, conceptual knowledge systems and the nebula interface. Technical Report. University of Arkansas, 1995
5. Corbett D, Burrow A L. Knowledge reuse in SEED exploiting conceptual graphs. In: Eklund P W, Ellis G, Mann G, eds. Proceeding of 4th International Conference on Conceptual Graphs (ICCS'96). Heidelberg: Springer, 1996. 56-60
6. ++: a detailed explanation. Inf Software Tech, 2007, 49(3): 212-229
7. Siff M, Reps T. Identifying modules via concept analysis. In: Harrold M J, Visaggio G, eds. Proceedings of International Conference on Software Maintenance. Washington, DC: IEEE Computer Society, 1997. 170-179
8. Ho T B. An approach to concept formation based on formal concept analysis. IEICE Trans Inf Syst, 1995, E782D(5): 553-559
9. Godin R. Incremental concept formation algorithm based on Galois (concept) lattices. Comp Intell, 1995, 11(2): 246-267
10. Yao Y Y. Concept lattices in rough set theory. In: Dick S, Kurgan L, Pedrycz W, et al. eds. Proceedings of 2004 Annual Meeting of the North American Fuzzy Information Processing Society (NAFIPS 2004). Washington, DC: IEEE Computer Society, 2004. 796-801
11. Saquer J, Deogun J S. Concept approximations based on rough sets and similarity measures. Appl Math Comp Sci, 2001, 11(3): 655-674
12. Tonella P. Using a concept lattice of decomposition slices for program understanding and impact analysis. IEEE Trans Software Eng, 2003, 29(6): 495-509
13. Grigoriev P A, Yevtushenko S A. Elements of an agile discovery environment. In: Grieser G, Tanaka Y, Yamamoto A, eds. Proceeding of 6th International Conference on Discovery Science (DS 2003). Berlin: Springer, 2003. 309-316
14. Gatzemeier F H, Meyer O. Text schema mining using graphs and formal concept analysis. In: Priss U, Corbett D, Angelova G, eds. Conceptual Structures - Integration and Interfaces (ICCS 2002). Berlin: Springer, 2002. 107-121
15. Zhang W X, Wei L, Qi J J. Attribute reduction theory and approach to concept lattice. Sci China Ser F-Inf Sci, 2005, 48(6): 713-726
16. Zhang W X, Qiu G F. Uncertain Decision Making Based on Rough Sets (in Chinese). Peking: Tsinghua University Press, 2005
17. Ganter B, Wille R. Formal Concept Analysis, Mathematical Foundations. Berlin: Springer, 1999
18. Wei L. Reduction theory and approach to rough set and concept lattice. Dissertation for the Doctoral Degree (in Chinese). Xi'an: Xi'an Jiaotong University, 2005