Narzędzia help

Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
first previous next last
cannonical link button


Fundamenta Informaticae

Tytuł artykułu

Attribute Reduction in Formal Contexts: A Covering Rough Set Approach

Autorzy Li, T-J.  Wu, W. Z. 
Treść / Zawartość
Warianty tytułu
Języki publikacji EN
EN This paper proposes an approach to attribute reduction in formal contexts via a covering rough set theory. The notions of reducible attributes and irreducible attributes of a formal context are first introduced and their properties are examined. Judgment theorems for determining all attribute reducts in the formal context are then obtained. According to the attribute reducts, all attributes of the formal context are further classified into three types and the characteristic of each type is characterized by the properties of irreducible classes of the formal context. Finally, by using the discernibility attribute sets, a method of distinguishing the reducible attributes and the irreducible attributes in formal contexts is presented.
Słowa kluczowe
EN attribute reduction   concept lattice   covering rough sets   formal contexts   rough set theory  
Wydawca IOS Press
Czasopismo Fundamenta Informaticae
Rocznik 2011
Tom Vol. 111, nr 1
Strony 15--32
Opis fizyczny Bibliogr. 37 poz., tab.
autor Li, T-J.
autor Wu, W. Z.
  • School of Mathematics, Physics and Information Science, Zhejiang Ocean University, Haiyuan Road 18, Dinghai, Zhoushan, Zhejiang 316000, P.R. China,
[1] Calegari, S., Ciucci, D.: Granular computing applied to ontologies, International Journal of Approximate Reasoning, 51, 2010, 391-409.
[2] Caspard, N., Monjardet, B.: The lattices of closure systems, closure operators, and implicational systems on a finite set: a survey, Discrete Applied Mathematics, 127, 2003, 241-269.
[3] Cornelis, C., Jensen, R., Hurtado, G., Slezak, D.: Attribute selection with fuzzy decision reducts, Information Sciences, 180, 2010, 209-224.
[4] Deogun, J. S., Saquer, J.: Concept approximations for formal concept analysis, Working with Conceptual Structures, Contributions to ICCS 2000, (G. Stumme, Ed.), Verlag Shaker Aachen, 2000, 73-83.
[5] Ganter, B., Meschke, C.: A formal concept analysis approach to rough data tables, In: RSFDGrC 2009 (H. Sakai et al., Eds.), LNAI, vol. 5908, Springer, Heidelberg, 2009, 117-126.
[6] Ganter, B., Wille, R.: Formal Concept Analysis, Mathematic Foundations, Springer: Berlin, 1999.
[7] Hu, K., Sui, Y., Lu, Y., Wang, J., Shi, C.: Concept approximation in concept lattice, In: Knowledge Discovery and Data Mining (D. Cheung, G.J. Williams, Q. Li, Eds.), PAKDD 2001, LNCS, vol. 2035, Springer, Heidelberg, 12, 2001, 167-173.
[8] Kryszkiewicz, M.: Comparative study of alternative types of knowledge reduction in insistent systems, International Journal of Intelligent Systems, 16, 2001, 105-120.
[9] Lai, H., Zhang, D.: Concept lattices of fuzzy contexts: Formal concept analysis vs. rough set theory, International Journal of Approximate Reasoning, 50, 2009, 695-707.
[10] Li, T.J.: Rough approximation operators in covering approximation spaces, In: RSCTC 2006 (S. Greco, et al., Eds.), LNCS, vol. 4259, Springer, Heidelberg, 2006, 174-182.
[11] Li, T.J.: Knowledge reduction in formal contexts based on covering rough sets, In: International Conference on Rough Sets and Knowledge Technology, LNCS vol. 5589, Springer, Heidelberg, 2009, 128-135.
[12] Li, T.J., Zhang,W.X.: Rough approximations in formal contexts, In: Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, 2005, 3162-3167.
[13] Liu, M., Shao, M.W., Zhang,W.X., Wu, C.: Reduction method for concept lattices based on rough set theory and its application, Computers and Mathematics with Applications, 53, 2007, 1390-1410.
[14] Mi, J.S., Leung, Y., Wu, W.Z.: Approaches to attribute reduction in concept lattices induced by axialities, Knowledge-Based Systems, 23, 2010, 504-511.
[15] Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reductions based on variable precision rough sets model, Information Sciences, 159 (3-4), 2004, 255-272.
[16] Patra, B.K., Nandi, S.: Fast single-link clustering method based on tolerance rough set model, In: RSFDGrC 2009 (H. Sakai et al., Eds.), LNAI vol. 5908, Springer, Heidelberg, 2009, 414-422.
[17] Pawlak, Z.: Rough sets, International Journal of Computer and Information Science, 11 ,1982, 341-356.
[18] Samanta, P., Chakraborty,M.K.: Covering based approaches to rough sets and implication lattices, In: RSFDGrC 2009( H. Sakai et al., Eds.), LNAI vol. 5908, Springer, Heidelberg, 2009, 127-134.
[19] Saquer, J., Deogun, J.: Concept approximations based on rough sets and similarity measures, International Journal of Applied Mathematics and Computer Science, 11, 2001, 655-674.
[20] Shao, M.W., Zhang, W.X.: The set approximation in formal contexts, In: RSFDGrC 2005 (D. Slezak et al., Eds.), LNCS vol. 3641, Springer, Heidelberg, 2005, 43-53.
[21] Shyng, J.Y., How-Ming Shieh, H.M., Tzeng, G.H.: An integrationmethod combining Rough Set Theory with formal concept analysis for personal investment portfolios, Knowledge-Based Systems, 23, 2010, 586-597.
[22] Slezak, D.: Degrees of conditional (in)dependence: A framework for approximate Bayesian networks and examples related to the rough set-based feature selection, Information Sciences, 179, 2009, 197-209.
[23] Slezak, D.,Wasilewski, P.: Granular sets-foundations and case study of tolerance spaces, In: RSFDGrC 2007 (A. An et al., Eds.), LNAI vol. 4482, Springer, Heidelberg, 2007, 435-442.
[24] Slezak, D., Ziarko, W.: The investigation of the Bayesian rough set model, International Journal of Approximate Reasoning, 40, 2005, 81-91.
[25] Wang, L., Liu, X.: A new model of evaluating concept similarity, Knowledge-Based Systems, 21 (8), 2008, 842-846.
[26] Wang,X., Zhang,W.X.: Relations of attribute reduction between object and property oriented concept lattices, Knowledge-Based Systems, 21, 2008, 398-403.
[27] Wei, L., Qi, J.J.: Relation between concept lattice reduction and rough set reduction, Knowledge-Based Systems, 23, 2010, 934-938.
[28] Wille, R.: Restructuring lattice theory: an approach based on hierarchies of concepts, In: Ordered Sets (I. Rival, Ed.), Reidel, Dordrecht, 1982, 445-470.
[29] Wu, W.Z.: Attribute reduction based on evidence theory in incomplete decision systems, Information Sciences, 178 (5), 2008, 1355-1371.
[30] Wu, W.Z., Leung, Y., Mi, J.S.: Granular computing and knowledge reduction in formal contexts, IEEE Transactions on Knowledge and Data Engineering, 21 (10), 2009, 1461-1474.
[31] Yao, Y.Y.: Concept lattices in rough set theory, In: Proceedings of 23rd International Meeting of the North American Fuzzy Information Processing Society, 13, 2004, 796-801.
[32] Yao, Y.Y.: A comparative study of formal concept analysis and rough set theory in data analysis, In: RSCTC 2004, LNCS vol. 3066, Springer, Heidelberg, 2004, 59-68.
[33] Zhang, W.X., Ma, J.M., Fan, S.Q.: Variable threshold concept lattices, Information Sciences, 177, 2007, 4883-4892.
[34] Zhang,W.X., Qiu, G.F., Wu, W.Z.: A general approach to attribute reduction in rough set theory, Science in China: Ser. F Information Sciences, 50 (2), 2007, 188-197.
[35] Zhang,W.X., Wei, L., Qi, J.J.: Attribute reduction theory and approach to concept lattice, Science in China: Ser. F Information Sciences, 48 (6), 2005, 713-726.
[36] Zhu, W. Wang, F.Y.: Reduction and axiomization of covering generalized rough sets, Information Sciences, 152, 2003, 217-230.
[37] Zhu, W., Wang, F.Y.: On three types of covering-based rough sets, IEEE Transactions on Knowledge and Data Engineering, 19 (8), 2007, 1131-1144.
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-article-BUS8-0020-0087