Influences of Three-Way Concept Lattice Caused by Variations of Attribute Values

Three-way concept lattices have been widely used in various types of applications. As the construction of three-way concept lattices is rather time consuming, especially for large formal contexts, it is not applicable to construct the lattices from the beginning when changes are made to the contexts. Motivated by this problem, the influences of three-way concept lattices caused by variations of attribute values are explored in this study. Specifically, we discuss two types of changes. One is changing the value of a specific incidence relation from 0 to 1, and the other is from 1 to 0. Furthermore, two types of three-way concept lattices are investigated. One is the object-induced three-way concept lattice, and the other is the attribute-induced three-way concept lattice. Both the mathematical proofs and the examples show the effectiveness of our proposed methods.

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

[2]  Yiyu Yao,et al.  Three-Way Formal Concept Analysis , 2014, RSKT.

[3]  Hamido Fujita,et al.  Updating three-way decisions in incomplete multi-scale information systems , 2019, Inf. Sci..

[4]  Jiafu Wan,et al.  Mining and updating association rules based on fuzzy concept lattice , 2017, Future Gener. Comput. Syst..

[5]  Ling Wei,et al.  The connections between three-way and classical concept lattices , 2016, Knowl. Based Syst..

[6]  Prem Kumar Singh,et al.  Three-way fuzzy concept lattice representation using neutrosophic set , 2017, Int. J. Mach. Learn. Cybern..

[7]  Xiao Zhang,et al.  On rule acquisition in decision formal contexts , 2013, Int. J. Mach. Learn. Cybern..

[8]  Yu-Lin He,et al.  Fuzziness based semi-supervised learning approach for intrusion detection system , 2017, Inf. Sci..

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

[10]  Sérgio M. Dias,et al.  Knowledge reduction in formal contexts using non-negative matrix factorization , 2015, Math. Comput. Simul..

[11]  Prem Kumar Singh Medical diagnoses using three-way fuzzy concept lattice and their Euclidean distance , 2018 .

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

[13]  Prem Kumar Singh,et al.  M-polar Fuzzy Graph Representation of Concept Lattice , 2018, Eng. Appl. Artif. Intell..

[14]  Jan Konecny,et al.  L-concept analysis with positive and negative attributes , 2016, Inf. Sci..

[15]  Ling Wei,et al.  The attribute reductions of three-way concept lattices , 2016, Knowledge-Based Systems.

[16]  Yu-Lin He,et al.  Fuzzy nonlinear regression analysis using a random weight network , 2016, Inf. Sci..

[17]  Jinhai Li,et al.  Granule description based knowledge discovery from incomplete formal contexts via necessary attribute analysis , 2019, Inf. Sci..

[18]  Yiyu Yao,et al.  An Outline of a Theory of Three-Way Decisions , 2012, RSCTC.

[19]  Bao Qing Hu,et al.  Three-way decisions space and three-way decisions , 2014, Inf. Sci..

[20]  Cherukuri Aswani Kumar,et al.  FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING , 2012, Appl. Artif. Intell..

[21]  Christopher G. Chute,et al.  Viewpoint Paper: Auditing the Semantic Completeness of SNOMED CT Using Formal Concept Analysis , 2009, J. Am. Medical Informatics Assoc..

[22]  Jinhai Li,et al.  Influence of dynamical changes on concept lattice and implication rules , 2018, Int. J. Mach. Learn. Cybern..

[23]  Jingtao Yao,et al.  Modelling Multi-agent Three-way Decisions with Decision-theoretic Rough Sets , 2012, Fundam. Informaticae.

[24]  Yiyu Yao,et al.  Interval sets and three-way concept analysis in incomplete contexts , 2016, International Journal of Machine Learning and Cybernetics.

[25]  Yiyu Yao,et al.  Three-way decision and granular computing , 2018, Int. J. Approx. Reason..

[26]  Yuhua Qian,et al.  Three-way cognitive concept learning via multi-granularity , 2017, Inf. Sci..

[27]  Xindong Wu,et al.  Efficient mining of both positive and negative association rules , 2004, TOIS.

[28]  Huilai Zhi,et al.  Granule description based on positive and negative attributes , 2018, Granular Computing.

[29]  Cherukuri Aswani Kumar,et al.  Role based access control design using three-way formal concept analysis , 2018, Int. J. Mach. Learn. Cybern..

[30]  Yiyu Yao,et al.  Decision-theoretic three-way approximations of fuzzy sets , 2014, Inf. Sci..

[31]  Guoyin Wang,et al.  Approximate concept construction with three-way decisions and attribute reduction in incomplete contexts , 2016, Knowl. Based Syst..

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

[33]  Amedeo Napoli,et al.  Mining gene expression data with pattern structures in formal concept analysis , 2011, Inf. Sci..

[34]  Paolo Tonella,et al.  Using a Concept Lattice of Decomposition Slices for Program Understanding and Impact Analysis , 2003, IEEE Trans. Software Eng..

[35]  Cherukuri Aswani Kumar,et al.  Three-way conceptual approach for cognitive memory functionalities , 2017, Int. J. Mach. Learn. Cybern..

[36]  Prem Kumar Singh,et al.  Similar Vague Concepts Selection Using Their Euclidean Distance at Different Granulation , 2018, Cognitive Computation.

[37]  Nouman Azam,et al.  Web-Based Medical Decision Support Systems for Three-Way Medical Decision Making With Game-Theoretic Rough Sets , 2015, IEEE Transactions on Fuzzy Systems.

[38]  Cherukuri Aswani Kumar,et al.  Bipolar fuzzy graph representation of concept lattice , 2014, Inf. Sci..

[39]  Zhang Yi,et al.  Incremental rough set approach for hierarchical multicriteria classification , 2018, Inf. Sci..