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International Journal of Machine Learning and Cybernetics
Published in: International Journal of Machine Learning and Cybernetics 5/2014

01-10-2014 | Original Article

Attribute reductions in object-oriented concept lattices

Authors: Jian-Min Ma, Yee Leung, Wen-Xiu Zhang

Published in: International Journal of Machine Learning and Cybernetics | Issue 5/2014

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Abstract

Attribute reduction is one of the main issues in the study of concept lattice. This paper mainly deals with attribute reductions of an object-oriented concept lattice constructed on the basis of rough set. Attribute rank of object-oriented concept lattice is first defined, and relationships between attribute rank and object-oriented concepts are then discussed. Based on attribute rank, generating algorithm of object-oriented concepts is investigated. The object-oriented consistent set and object-oriented reduction of an object-oriented concept lattice are defined. Adjustment theorems of the object-oriented consistent set, and the necessary and sufficient conditions for a attribute subset to be an object-oriented consistent set of an object-oriented concept lattice are discussed. Then the object-oriented discernibility matrix of an object-oriented concept lattice is defined and its properties are also studied. Based on the object-oriented discernibility matrix, an approach to object-oriented reductions of an object-oriented concept lattice is proposed, and the attribute characteristics are also analyzed.
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Literature
1.
Carpineto C, Romano G (2004) Exploiting the potential of concept lattices for information retrieval with CREDO. J Univers Comput Sci 10(8):985–1013MATH
2.
Cole R, Eklund P, Stumme G (2003) Document retrieval for e-mail search and discovery using formal concept analysis. Appl Artif Intell 17(3):257–280CrossRef
3.
Elloumi S, Jaam J, Hasnah A, Jaoua A, Nafkha I (2004) A multi-level conceptual data reduction approach based on the Lukasiewicz implication. Inf Sci 163(4):253–262CrossRefMATHMathSciNet
4.
Ganter B, Wille R (1999) Formal concept analysis. Mathematical Foundations. Springer, Berlin
5.
Gediga G, Duentsch I (2002) Modal-style operators in qualitative data analysis. In: Proceedings of the 2002 IEEE international conference on data mining, pp 155–162
6.
Hereth J, Stumme G, Wille R, Wille U (2000) Conceptual knowledge discovery and data analysis. Lecture Notes in Artificial Intelligence 1867. Springer, Berlin, pp 421–437
7.
Hu K, Sui Y, Lu Y, Wang J, Shi C (2001) Concept approximation in concept lattice. In: Cheung D, Williams GJ, Q Li (eds) Knowledge discovery and data mining, proceedings of the 5th Pacific- Asia conference, PAKDD 2001. Lecture Notes in Computer Science 2035. Springer, London, pp 167–173
8.
9.
Kent RE (1996) Rough concept analysis: a synthesis of rough sets and formal concept analysis. Fundam Inf 27:169–181MATHMathSciNet
10.
Kim M, Compton P (2004) Evolutionary document management and retrieval for specialized domains on the web. Int J Hum Comput Stud 60(2):201–241CrossRef
11.
Kryszkiewicz M (2001) Comparative study of alternative types of knowledge reduction in insistent systems. Int J Intell Syst 16:105–120CrossRefMATH
12.
Kuznetsov SO (2004) Machine learning and formal concept analysis. In: Eklund P (ed) ICFCA 2004. Lecture Notes in artificial intelligence 2961. Springer, Berlin, pp 287–312
13.
Leung Y, Wu WZ, Zhang WX (2006) Knowledge acquisition in incomplete information systems: a rough set approach. Eur J Oper Res 168(1):164–180CrossRefMATHMathSciNet
14.
Li JH, Mei CL, Cherukuri AK, Zhang X (2013) On rule acquisition in decision formal contexts. Int J Mach Learn Cybern 4(6):721–731CrossRef
15.
Li JH, Mei CL, Lv YJ (2013) Incomplete decision contexts: approximate concept construction, rule acquisition and knowledge reduction. Int J Approx Reason 54(2): 149–165CrossRefMATHMathSciNet
16.
Li LF, Zhang JK (2010) Attribute reduction in fuzzy concept lattices based on the T implication. Knowl Based Syst 23:497–503CrossRef
17.
Ma JM, Zhang WX, Wang X (2006) Dependence space of concept lattices based on rough set. In: 2006 IEEE international conference on granular computing, pp 20–204
18.
Maji P, Paul S (2012) Rough-fuzzy clustering for grouping functionally similar genes from microarray data. In: IEEE/ACM transactions on computational biology and bioinformatics, pp 1–14
19.
Maji P, Paul S (2011) Rough set based maximum relevance-maximum significance criterion and gene selection from microarray data. Int J Approx Reason 52(3):408–426CrossRef
20.
Maji P, Paul S (2010) Rough sets for selection of molecular descriptors to predict biological activity of molecules. IEEE Trans Syst Man Cybern Part C Appl Rev 40(6):639–648CrossRef
21.
Maji P, Pal SK (2010) Feature selection using f-information measures in fuzzy approximation spaces. IEEE Trans Knowl Data Eng 22(6):854–867CrossRef
22.
Maji P, Pal SK (2007) Rough set based generalized fuzzy C-means algorithm and quantitative indices. IEEE Trans Syst Man Cybern Part B Cybern 37(6):1529–1540CrossRef
23.
Maji P., Pal SK (2007) Rough-fuzzy C-medoids algorithm and selection of bio-basis for amino acid sequence analysis. IEEE Trans Knowl Data Eng 19(6):859–872CrossRef
24.
Mi JS, Wu WZ, Zhang WX (2004) Approaches to knowledge reductions based on variable precision rough sets model. Inf Sci 159(3-4):255–272CrossRefMATHMathSciNet
25.
Mi JS, Leung Y, Wu WZ (2010) Approaches to attribute reduction in concept lattices induced by axialities. Knowl Based Syst 23:504–511CrossRef
26.
Pagliani P (1993) From concept lattices to approximation spaces: algebraic structures of some spaces of partial objects. Fundam Inf 18:1–25MATHMathSciNet
27.
Paul S, Maji P (2012) Gene ontology based quantitative index to select functionally diverse genes. Int J Mach Learn Cybern 1–17
28.
29.
Pasquier N, Bastide Y, Taouil R, Lakhal L (1999) Ecient mining of association rules using closed itemset lattices. Inf Syst 24(1):25–46CrossRef
30.
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 the rough sets theory. Kluwer, Boston, pp 331–362
31.
Stumme G, Taouil R, Bastide Y, Pasquier N, Lakhal L (2002) Computing iceberg concept lattices with TITANIC. Data Knowl Eng 42(2):189–222CrossRefMATH
32.
Taylor PJ, Catalano ., Walker DRF (2002) Measurement of the world City network. Urban Stud 39(13):2367–2376CrossRef
33.
Valtchev P, Missaoui R, Godin R (2004) Formal concept analysis for knowledge discovery and data mining: the new challenges. In: Eklund P (ed) ICFCA 2004. Lecture Notes in artificial intelligence 2961. Springer, Berlin, pp 352–371
34.
Wei L, Qi JJ, Zhang WX (2008) Attribute reduction theory of concept lattice based on decision formal contexts, science in China. Ser F C Inf Sci 51(7):910C923MathSciNet
35.
Wille R (1982) Restructuring lattice theory: an approach based on hierarchies of concepts. In: Rival I (ed) Ordered sets. Reidel, Dordrecht, pp 445–470
36.
Wolff KE (2001) A conceptual view of knowledge bases in rough set theory. In: Ziarko W, Yao YY (eds) Rough sets and current trends in computing 2000. Lecture Notes in computer science 2005. Springer, Berlin, pp 220–228
37.
Wu WZ, Zhang M, Li HZ, Mi JS (2005) Knowledge reduction in random information systems via Dempster-Shafer theory of evidence. Inf Sci 174(3-4):143–164CrossRefMATHMathSciNet
38.
Wu WZ, Leung Y, Mi JS (2009) Granular computing and knowledge reduction in formal contexts. IEEEE Trans Knowl Data Eng 21(10):461–1474
39.
Yao YY (2004) Concept lattices in rough set theory. In: Dick S, Kurgan L, Pedrycz W, Reformat M (eds) Proceedings of 2004 annual meeting of the North American fuzzy information processing society (NAFIPS 2004),pp 796–801
40.
Yao YY (2004) A Comparative study of formal concept analysis and rough set theory in data analysis. In: Tsumoto S, Slowinski R, Jan Komorowski H, Grzymala-Busse JW (eds) Rough sets and current trends in computing 2004, LNAI 3066, Springer, Berlin, pp 59–68
41.
Yao YY, Chen YH (2004) Rough set approximations in formal concept analysis. In: Dick S, Kurgan L, Pedrycz W, Reformat M (eds) Proceedings of 2004 annual meeting of the North American fuzzy information processing society (NAFIPS 2004), June 27–30, pp 73–78
42.
Metadata
Title
Attribute reductions in object-oriented concept lattices
Authors
Jian-Min Ma
Yee Leung
Wen-Xiu Zhang
Publication date
01-10-2014
Publisher
Springer Berlin Heidelberg
Published in
International Journal of Machine Learning and Cybernetics / Issue 5/2014
Print ISSN: 1868-8071
Electronic ISSN: 1868-808X
DOI
https://doi.org/10.1007/s13042-013-0214-0

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