Parallel computing techniques for concept-cognitive learning based on granular computing

Concept-cognitive learning, as an interdisciplinary study of concept lattice and cognitive learning, has become a hot research direction among the communities of rough set, formal concept analysis and granular computing in recent years. The main objective of concept-cognitive learning is to learn concepts from a give clue with the help of cognitive learning methods. Note that this kind of studies can provide concept lattice insight to cognitive learning. In order to deal with more complex data and improve learning efficiency, this paper investigates parallel computing techniques for concept-cognitive learning in terms of large data and multi-source data based on granular computing and information fusion. Specifically, for large data, a parallel computing framework is designed to extract global granular concepts by combining local granular concepts. For multi-source data, an effective information fusion strategy is adopted to obtain final concepts by integrating the concepts from all single-source data. Finally, we conduct some numerical experiments to evaluate the effectiveness of the proposed parallel computing algorithms.