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Granular computing (GrC) is a methodology for reducing the complexity of problem solving and includes two basic aspects: granulation and granular-based computing. Strongly connected components (SCCs) are a significant subgraph structure in digraphs. In this paper, two new granulation strategies were devised to improve the efficiency of computing SCCs. Firstly, four SCC correlations between the vertices were found, which can be divided into two classes. Secondly, two granulation strategies were designed based on correlations between two classes of SCCs. Thirdly, according to the characteristics of the granulation results, the parallelization of computing SCCs was realized. Finally, a parallel algorithm based on granulation strategy for computing SCCs of simple digraphs named GPSCC was proposed. Experimental results show that GPSCC performs with higher computational efficiency than algorithms. [ABSTRACT FROM AUTHOR]

In the era of big data for exploring attribute reduction/rough set-based feature selection related problems, to design efficient strategies for deriving reducts and then reduce the dimensions of data, two fundamental perspectives of Granular Computing may be taken into account: breaking up the whole into pieces and gathering parts into a whole. From this point of view, a novel strategy named label-specific guidance is introduced into the process of searching reduct. Given a formal description of attribute reduction, by considering the corresponding constraint, we divide it into several label-specific based constraints. Consequently, a sequence of these label-specific based constraints can be obtained, it follows that the reduct related to the previous label-specific based constraint may have guidance on the computation of that related to the subsequent label-specific based constraint. The thinking of this label-specific guidance runs through the whole process of searching reduct until the reduct over the whole universe is derived. Compared with five state-of-the-art algorithms over 20 data sets, the experimental results demonstrate that our proposed acceleration strategy can not only significantly accelerate the process of searching reduct but also offer justifiable performance in the task of classification. This study suggests a new trend concerning the problem of quickly deriving reduct. [ABSTRACT FROM AUTHOR]