Abstract
As one aspect of granular computing, hierarchical knowledge granularity can speed up solution, and reduce computational complexity. This paper describes the structure and hierarchy analysis of granularity simply, details the current methods of construction algorithms in granular computing, and emphasizes the performance comparisons of various construction algorithms, and finally reviews the applications of knowledge granularity in rule extraction, attribute reduction, cluster analysis, optimization theory, neural network and fuzzy control and so on.
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