Cognitive concept learning via granular computing for big data

In this paper, analysis of time complexities of existing granular-computing-based cognitive concept learning method-s is provided by numerical experiments. Moreover, technical problems are addressed when the existing cognitive concept learning methods are applied to big data.

[1]  Yiyu Yao,et al.  Interpreting Concept Learning in Cognitive Informatics and Granular Computing , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[2]  Jinhai Li,et al.  Incomplete decision contexts: Approximate concept construction, rule acquisition and knowledge reduction , 2013, Int. J. Approx. Reason..

[3]  Qingguo Li,et al.  Power contexts and their concept lattices , 2011, Discret. Math..

[4]  Witold Pedrycz,et al.  Granular Computing: Analysis and Design of Intelligent Systems , 2013 .

[5]  Jinhai Li,et al.  Knowledge reduction in decision formal contexts , 2011, Knowl. Based Syst..

[6]  Wen-Xiu Zhang,et al.  A mathematical model for concept granular computing systems , 2010, Science China Information Sciences.

[7]  Yiyu Yao,et al.  On the System Algebra Foundations for Granular Computing , 2009, Int. J. Softw. Sci. Comput. Intell..

[8]  Lotfi A. Zadeh,et al.  Fuzzy sets and information granularity , 1996 .

[9]  Winston A Hide,et al.  Big data: The future of biocuration , 2008, Nature.

[10]  Manuel Ojeda-Aciego,et al.  Dual multi-adjoint concept lattices , 2013, Inf. Sci..

[11]  Xindong Wu,et al.  Data mining with big data , 2014, IEEE Transactions on Knowledge and Data Engineering.

[12]  Weihua Xu,et al.  A novel cognitive system model and approach to transformation of information granules , 2014, Int. J. Approx. Reason..

[13]  Adam Jacobs,et al.  The pathologies of big data , 2009, Commun. ACM.

[14]  Yuhua Qian,et al.  Concept learning via granular computing: A cognitive viewpoint , 2014, Information Sciences.

[15]  Chu Kiong Loo,et al.  Formal concept analysis approach to cognitive functionalities of bidirectional associative memory , 2015, BICA 2015.

[16]  WenQi Liu Modeling data quality control system for Chinese public database and its empirical analysis , 2014 .

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

[18]  Ming-Wen Shao,et al.  Rule acquisition and attribute reduction in real decision formal contexts , 2011, Soft Comput..

[19]  Ivo Düntsch,et al.  Approximation Operators in Qualitative Data Analysis , 2003, Theory and Applications of Relational Structures as Knowledge Instruments.

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

[21]  Yingxu Wang,et al.  On Cognitive Computing , 2009, Int. J. Softw. Sci. Comput. Intell..

[22]  Weihua Xu,et al.  Granular Computing Approach to Two-Way Learning Based on Formal Concept Analysis in Fuzzy Datasets , 2016, IEEE Transactions on Cybernetics.

[23]  Wen-Xiu Zhang,et al.  Axiomatic characterizations of dual concept lattices , 2013, Int. J. Approx. Reason..

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

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

[26]  Yiyu Yao,et al.  MGRS: A multi-granulation rough set , 2010, Inf. Sci..