Relations of attribute reduction between object and property oriented concept lattices

As one of the basic problems of knowledge discovery and data analysis, knowledge reduction can make the discovery of implicit knowledge in data easier and the representation simpler. In this paper, relations of attribute reduction between object and property oriented formal concept lattices are discussed. And beautiful results are obtained that attribute reducts and attribute characteristics in the two concept lattices are the same based on new approaches to attribute reduction by means of irreducible elements. It turns out to be meaningful and effective in dealing with knowledge reduction, as attribute reducts and attribute characteristics in the object and property oriented formal concept lattices can be acquainted by only investigating one of the two concept lattices.

[1]  Jitender S. Deogun,et al.  Formal Rough Concept Analysis , 1999, RSFDGrC.

[2]  Ju Wang,et al.  Concept Approximation in Concept Lattice , 2001, PAKDD.

[3]  Yiyu Yao,et al.  A Comparative Study of Formal Concept Analysis and Rough Set Theory in Data Analysis , 2004, Rough Sets and Current Trends in Computing.

[4]  Yiyu Yao,et al.  Rough set approximations in formal concept analysis , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[5]  Ivo Düntsch,et al.  Modal-style operators in qualitative data analysis , 2002, 2002 IEEE International Conference on Data Mining, 2002. Proceedings..

[6]  Wei-Zhi Wu,et al.  Approaches to knowledge reduction based on variable precision rough set model , 2004, Inf. Sci..

[7]  Wei-Zhi Wu,et al.  Approaches to knowledge reductions in inconsistent systems , 2003, Int. J. Intell. Syst..

[8]  Ming-Wen Shao,et al.  Approximation in Formal Concept Analysis , 2005, RSFDGrC.

[9]  Xia Wang,et al.  A Novel Approach to Attribute Reduction in Concept Lattices , 2006, RSKT.

[10]  Yiyu Yao,et al.  Concept lattices in rough set theory , 2004, IEEE Annual Meeting of the Fuzzy Information, 2004. Processing NAFIPS '04..

[11]  Radim Belohlávek,et al.  Crisply Generated Fuzzy Concepts , 2005, ICFCA.

[12]  Wen-Xiu Zhang,et al.  Attribute Reduction in Concept Lattice Based on Discernibility Matrix , 2005, RSFDGrC.

[13]  Bernhard Ganter,et al.  Formal Concept Analysis: Mathematical Foundations , 1998 .

[14]  T. Y. Lin,et al.  Rough Sets and Data Mining , 1997, Springer US.

[15]  Rudolf Wille,et al.  Restructuring Lattice Theory: An Approach Based on Hierarchies of Concepts , 2009, ICFCA.

[16]  Jerzy W. Grzymala-Busse,et al.  Rough Sets , 1995, Commun. ACM.

[17]  Z. Pawlak Rough Sets: Theoretical Aspects of Reasoning about Data , 1991 .

[18]  J. Deogun,et al.  Concept approximations based on rough sets and similarity measures , 2001 .

[19]  Mohamed Quafafou,et al.  alpha-RST: a generalization of rough set theory , 2000, Inf. Sci..

[20]  Radim Belohlávek,et al.  Formal Concept Analysis Constrained by Attribute-Dependency Formulas , 2005, ICFCA.

[21]  Malcolm J. Beynon,et al.  Reducts within the variable precision rough sets model: A further investigation , 2001, Eur. J. Oper. Res..

[22]  Wei-Zhi Wu,et al.  Knowledge reduction in random information systems via Dempster-Shafer theory of evidence , 2005, Inf. Sci..