Granular Computing (GrC) is a general computation theory for effectively using granules such as classes, clusters, subsets, groups and intervals to build an efficient computational model for complex applications with huge amounts of data, information and knowledge. It demonstrates different levels of abstraction in getting a trade-off between complexity and accuracy. The granulation levels may include value, variable, component, system, concept, or class granulation. The basic notions and principles of granular computing have appeared in many related fields, such as information hiding in programming, granularity in artificial intelligence, divide and conquer in theoretical computer science, interval computing, cluster analysis, fuzzy and rough set theories, neutrosophic computing, quotient space theory, belief functions, machine learning, databases, and many others. This special session provides a good opportunity for researchers on granular computing and other areas to exchange their ideas and develop the approaches of GrC.