Yang, Hong-Zhi; Yee, Leung; Shao, Ming-Wen Rule acquisition and attribute reduction in real decision formal contexts. (English) Zbl 1237.68218 Soft Comput. 15, No. 6, 1115-1128 (2011). Summary: Formal Concept Analysis of real set formal contexts is a generalization of classical formal contexts. By dividing the attributes into condition attributes and decision attributes, the notion of real decision formal contexts is introduced. Based on an implication mapping, problems of rule acquisition and attribute reduction of real decision formal contexts are examined. The extraction of “if-then” rules from the real decision formal contexts, and the approach to attribute reduction of the real decision formal contexts are discussed. By the proposed approach, attributes which are non-essential to the maximal \(s\) rules or \(l\) rules (to be defined later in the text) can be removed. Furthermore, discernibility matrices and discernibility functions for computing the attribute reducts of the real decision formal contexts are constructed to determine all attribute reducts of the real set formal contexts without affecting the results of the acquired maximal \(s\) rules or \(l\) rules. Cited in 8 Documents MSC: 68T30 Knowledge representation Keywords:attribute reduction; concept lattice; formal concept analysis; real relation; rules acquisition PDF BibTeX XML Cite \textit{H.-Z. Yang} et al., Soft Comput. 15, No. 6, 1115--1128 (2011; Zbl 1237.68218) Full Text: DOI References: [1] Belohlavek R (2001) Fuzzy closure operators. I. J Math Anal Appl 262:473–489 · Zbl 0989.54006 [2] Belohlavek R (2002) Logic precision in concept lattices. J Logic Comput 12:137–148 · Zbl 1065.06005 [3] Belohlavek R (2004) Concept lattice and order in fuzzy logic. Ann Pure Appl Logic 128(1–3):277–298 · Zbl 1060.03040 [4] Beynon M (2001) Reducts within the variable precision rough sets model: a further investigation. Eur J Oper Res 134:592–605 · Zbl 0984.90018 [5] Burusco A, Fuentes-González R (2000) Concept lattices defined from implication operators. Fuzzy Sets Syst 114(3):431–436 · Zbl 0971.06010 [6] Carpineto C, Romano G (1996) A lattice conceptual clustering system and its application to browsing retrieval. Mach Learn 10:95–122 [7] Chen D, Wang C, Hu Q (2007) A new approach to attribute reduction of consistent and inconsistent covering decision systems with covering rough sets. Info Sci 177:3500–3518 · Zbl 1122.68131 [8] Elloumi S, Jaam J, Hasnah A, Jaoua A, Nafkha I (2004) A multi-level conceptual data reduction approach based on the Lukasiewicz implication. Info Sci 163:253–262 · Zbl 1076.68085 [9] Faid M, Missaoi R, Godin R (1997) Mining complex structures using context concatenation in formal concept analysis. International KRUSE Symposium, Vancouver, BC, pp 11–13 [10] Gediga B, Wille R (1999) Formal concept analysis, mathematic foundations. Springer, Berlin [11] Georgescu G, Popescu A (2004) Non-dual fuzzy connections. Arch Math Logic 43(8):1009–1039 · Zbl 1060.03042 [12] Godin R, Missaoi R (1994) An incremental concept formation approach for learning from databases. Theor Comput Sci 133:387–419 · Zbl 0938.68806 [13] Harms SK, Deogum JS (2004) Sequential association rule mining with time lags. J Intell Info Syst 22(1):7–22 · Zbl 02040419 [14] Hu QH, Xie ZX, Yu DR (2007) Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation. Pattern Recogn 40(12):3509–3521 · Zbl 1129.68073 [15] Jaoua A, Elloumi S (2002) Galois connection, formal concepts and Galois lattice in real relations: application in a real classifier. J Syst Softw 60:149–163 · Zbl 05433019 [16] Ke LJ, Feng ZR, Ren ZG (2008) An efficient ant colony optimization approach to attribute reduction in rough set theory. Pattern Recogn Lett 29(9):1351–1357 [17] Krajči S (2003) Cluster based efficient generation of fuzzy concepts. Neural Netw World 5:521–530 [18] Liu M, Shao MW, Zhang WX, Wu C (2007) Reduction method for concept lattices based on rough set theory and its application. Comput Math Appl 53(9):1390–1410 · Zbl 1121.68113 [19] Mi JS, Wu WZ, Zhang WX (2004) Approaches to knowledge reductions based on variable precision rough sets model. Info Sci 159(3–4):255–272 · Zbl 1076.68089 [20] Popescu A (2001) A general approach to fuzzy concept. Math Logic Q 50(3):1–17 [21] Skowron A (1993) A synthesis of decision rules: applications of discernibility matrix. In: Proceedings of the international conference on intelligent information systems, Augustow, Poland, pp 30–46 [22] Skowron A, Rauszer C (1992) The discernibility matrices and functions in information systems. In: Slowinski R (ed) Intelligent decision support: handbook of applications and advances of rough sets theory. Kluwer, Dordrecht, pp 331–362 [23] Starzyk JA, Nelson DE, Sturtz K (2000) A mathematical foundation for improved reduct generation in information systems. Knowl Info Syst 2:131–146 · Zbl 1002.68601 [24] Wang GY (2003) Rough reduction in algebra view and information view. Int J Intell Syst 18:679–688 · Zbl 1037.68138 [25] Wang X, Zhang WX (2008) Relations of attribute reduction between object and property oriented concept lattices. Knowl Based Syst 21(5):398–403 [26] Wei L, Qi JJ, Zhang WX (2008) Attribute reduction theory of concept lattice based on decision formal contexts. Sci China Ser F Info Sci 51(7):910–923 · Zbl 1291.68391 [27] Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel, Dordrecht, pp 445–470 [28] Wu WZ (2008) Attribute reduction based on evidence theory in incomplete decision systems. Info Sci 178(5):1355–1371 · Zbl 1134.68056 [29] Wu WZ, Zhang M, Li HZ, Mi JS (2005) Knowledge reductions in random information systems via Dempster–Shafer theory of evidence. Info Sci 174(3–4):143–165 · Zbl 1088.68169 [30] Wu WZ, Yee Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEE Trans Knowl Data Eng 21(10): 1461–1474 [31] Yahia S, Jaoua A (2001) Discovering knowledge from fuzzy concept lattice[A]. In: Kandel A, Last M, Bunke H (ed) Data mining and computational intelligence[C], Physica-Verlag, Heidelberg, pp 167–190 [32] Yang XB, Yang JY, Wu C et al (2008) Dominance-based rough set approach and knowledge reductions in incomplete ordered information system. Info Sci 178(4):1219–1234 · Zbl 1134.68057 [33] Zhang WX, Wu WZ, Liang JY, Li DY (2001) Theory and method of rough sets. Science Press, Beijing [34] Zhang WX, Mi JS, Wu WZ (2003) Approaches to knowledge reductions in inconsistent systems. Int J Intell Syst 21:989–1000 · Zbl 1069.68606 [35] Zhang WX, Wei L, Qi JJ (2005) Attribute reduction theory and approach of concept lattices. Sci China Ser E Info Sci 35(6):628–639 [36] Zhang WX, Ma JM, Fan SQ (2007) Variable threshold concept lattices. Info Sci 17(22):4883–4892 · Zbl 1130.06004 This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.