Concept lattice is the key mathematical tool in formal concept analysis to make data analysis. Granular computing is a useful mathematical paradigm for designing information granules. In recent years, concept lattice and granular computing models and algorithms have attracted much attention from the communities of formal concept analysis, rough set theory and big data due to their abilities to deal with uncertainty, impreciseness and fuzziness in knowledge discovery and data mining processes. Advances on the combination of concept lattice and granular computing can be beneficial to both of these theories, and many promising research directions have been developed such as granular computing based concept learning theories, cognitive learning models of granular concepts, and conceptual knowledge discovery algorithms for dealing with big data. Besides, the integration of concept lattice and granular computing lead to many novel conceptual knowledge representation models. For example, multi-granularity models of concept lattice, multi-layered models of concept lattice, and multi-level conceptual knowledge extraction models for relational databases. Consequently, the use of concept lattice and granular computing could add value to the study of big data compared to the single theory presently employed. Due to the rapid advances in dealing with big data, concept lattice and granular computing models and algorithms were explored in depth. Thus, this special section intends to facilitate readers for gaining the recent advances in mathematical foundations, theories, methods and applications of concept lattice and granular computing for dealing with big data. We invite scholars to contribute their original research articles that will seek the continuing efforts to share the new ideas and recent trends in dealing with big data.

Topics of Interest

Topics include, but are not limited to the following:

Mathematical foundations of formal concept analysis for incremental computing
Mathematical models of granular computing for parallel computing
Axiomatic approaches of rough set theory and formal concept analysis
Uncertainty measure in big data
Fuzziness application in big data
Multi-granularity models of concept lattice
Multi-layered models of concept lattice
Cognitive concept learning under the environment of big data
Granular computing based concept learning algorithms for big data
Multi-granularity classification for big data
Rough set theory for big data
Three-way concept analysis theory and method

Submission Instructions

Full papers must be written in English and should be submitted at https://www.amcs.uz.zgora.pl/?action=submission (all manuscripts should follow the submission guidelines available at the web site). More importantly, author(s) should list Spec_Sec_Granular as one keyword of the submission, so Editors know that the submission is for our special section.

Important Dates

Deadline for paper submission: May 1, 2017

Review Notification to authors: July 1, 2017

Submission of revised papers: September 1, 2017

Notification of final review results: October 1, 2017

Guest Editors

Dr. Jinhai Li, Kunming University of Science and Technology, Kunming, China

Email: jhlixjtu@163.com

Dr. Ch. Aswani Kumar, VIT University, Vellore, India

Email: cherukuri@acm.org

Dr. Jonas Poelmans, P2Commodities, United Kingdom,

Email: jonas.poelmans@gmail.com

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