Flexible self-learning model based on granular computing
一种可伸缩的粒计算知识获取方法

Aiming at the problem of main memory limit when Granular Computing algorithm processes mass data sets, a concept of Granular Distribution List (GDL) was put forward based on the analysis of the hierarchical granular model of information table, and a Granular Computing algorithm was improved by using this list. Experiments demonstrated the revised algorithm can process mass datasets directly without influencing the validity of the original algorithm.