一种可伸缩的粒计算知识获取方法

针对粒计算算法在处理海量数据时所面临的内存限制问题，通过对信息表分层粒化模型的研究，提出了一种粒分布链表结构的概念，并利用该链表结构改进了一个粒计算算法。通过实验验证，改进后的算法能够直接处理海量数据集，同时不影响原算法的有效性。

Flexible self-learning model based on granular computing

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.