A new algebraic structure for formal concept analysis

Formal concept analysis (FCA) originally proposed by Wille 39, is an important theory for data analysis and knowledge discovery. Concept lattice is the core of the mathematical theory of formal concept analysis. To address the requirements of real word applications, concept lattice has been extended to many other forms from the theoretical point of view and possible applications. In this paper, with the aim of deriving the mathematical properties of formal concepts from the point of algebra, we propose a new algebra system for the formal context. Under the frame of the proposed system, some interesting properties of formal concepts are explored, which could be applied to explore concept hierarchy and ontology merging.

[1]  Ming-Wen Shao,et al.  Set approximations in fuzzy formal concept analysis , 2007, Fuzzy Sets Syst..

[2]  Yee Leung,et al.  Granular Computing and Knowledge Reduction in Formal Contexts , 2009, IEEE Transactions on Knowledge and Data Engineering.

[3]  Xia Wang,et al.  Rough Ontology Mapping in E-Business Integration , 2007, E-Service Intelligence.

[4]  Ming-Wen Shao,et al.  Reduction method for concept lattices based on rough set theory and its application , 2007, Comput. Math. Appl..

[5]  Vilém Vychodil,et al.  Formal Concept Analysis With Background Knowledge: Attribute Priorities , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[6]  Dexue Zhang,et al.  Concept lattices of fuzzy contexts: Formal concept analysis vs. rough set theory , 2009, Int. J. Approx. Reason..

[7]  Ch. Aswanikumar,et al.  Concept lattice reduction using fuzzy K-Means clustering , 2010, Expert Syst. Appl..

[8]  Andrei Popescu,et al.  Non-commutative fuzzy Galois connections , 2003, Soft Comput..

[9]  Jean-Marc Champarnaud,et al.  Theoretical study and implementation of the canonical automaton , 2002, Fundam. Informaticae.

[10]  Claudio Carpineto,et al.  A Lattice Conceptual Clustering System and Its Application to Browsing Retrieval , 1996, Machine Learning.

[11]  Yiyu Yao,et al.  A multiview approach for intelligent data analysis based on data operators , 2008, Inf. Sci..

[12]  Anna Formica,et al.  Ontology-based concept similarity in Formal Concept Analysis , 2006, Inf. Sci..

[13]  Jitender S. Deogun,et al.  Monotone concepts for formal concept analysis , 2004, Discret. Appl. Math..

[14]  Vilém Vychodil,et al.  Discovery of optimal factors in binary data via a novel method of matrix decomposition , 2010, J. Comput. Syst. Sci..

[15]  Guy W. Mineau,et al.  Automatic Structuring of Knowledge Bases by Conceptual Clustering , 1995, IEEE Trans. Knowl. Data Eng..

[16]  Patrick Brézillon,et al.  Lecture Notes in Artificial Intelligence , 1999 .

[17]  Derek G. Bridge,et al.  Collaborative Recommending using Formal Concept Analysis , 2006, Knowl. Based Syst..

[18]  Gerd Stumme,et al.  Computing iceberg concept lattices with T , 2002, Data Knowl. Eng..

[19]  Jesús Medina,et al.  Relating generalized concept lattices and concept lattices for non-commutative conjunctors , 2008, Appl. Math. Lett..

[20]  Michael Bain,et al.  Inductive Construction of Ontologies from Formal Concept Analysis , 2003, Australian Conference on Artificial Intelligence.

[21]  Hong Wang,et al.  Approaches to knowledge reduction in generalized consistent decision formal context , 2008, Math. Comput. Model..

[22]  Manuel Ojeda-Aciego,et al.  Towards Biresiduated Multi-adjoint Logic Programming , 2003, CAEPIA.

[23]  Wei Xu,et al.  Fuzzy inference based on fuzzy concept lattice , 2006, Fuzzy Sets Syst..

[24]  Nicolas Pasquier,et al.  Efficient Mining of Association Rules Using Closed Itemset Lattices , 1999, Inf. Syst..

[25]  Radim Belohlávek,et al.  Optimal triangular decompositions of matrices with entries from residuated lattices , 2009, Int. J. Approx. Reason..

[26]  Ramón Fuentes-González,et al.  The study of the L-fuzzy concept lattice , 1994 .

[27]  Sergei O. Kuznetsov,et al.  Machine Learning and Formal Concept Analysis , 2004, ICFCA.

[28]  Xiaodong Liu,et al.  Concept analysis via rough set and AFS algebra , 2008, Inf. Sci..

[29]  Manuel Ojeda-Aciego,et al.  On Multi-adjoint Concept Lattices: Definition and Representation Theorem , 2007, ICFCA.

[30]  Uta Priss Formal concept analysis in information science , 2006 .

[31]  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.

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

[33]  Andrei Popescu,et al.  Concept lattices and similarity in non-commutative fuzzy logic , 2002, Fundam. Informaticae.

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

[35]  Xiaodong Liu,et al.  Approaches to the representations and logic operations of fuzzy concepts in the framework of axiomatic fuzzy set theory I , 2007, Inf. Sci..

[36]  Radim Belohlávek,et al.  Concept lattices and order in fuzzy logic , 2004, Ann. Pure Appl. Log..

[37]  Michel C. A. Klein,et al.  The semantic web: yet another hip? , 2002, Data Knowl. Eng..

[38]  Václav Snásel,et al.  Concept Lattice Reduction by Singular Value Decomposition , 2007, SYRCoDIS.

[39]  Michel Liquière,et al.  Algebraic results and bottom-up algorithm for policies generalization in reinforcement learning using concept lattices , 2008 .