Graph-theory-based approaches have been used with great success when analyzing abstract properties of natural and artificial networks. However, these approaches have not factored...
Manifold learning can discover the structure of high dimensional data and provides understanding of multidimensional patterns by preserving the local geometric characteristics. Ho...
Granular computing is gradually changing from a label to a new field of study. The driving forces, the major schools of thought, and the future research directions on granular co...
Feature selection is an important problem for pattern classification systems. Mutual information is a good indicator of relevance between variables, and has been used as a measure...
This is a follow-up of the paper “A ten-year review of granular computing” published in 2007. We will continue to examine the most influential papers in granular computing. B...