Granular computing,as a novel information processing method,can uncover multi-level and multi-perspective knowledge hidden within data,and has found extensive applications in various fields.Based on granular computing theory,the granular ball computing method utilizes hierarchical clustering to granulate information with the purity of granular balls as the criterion,using these granular balls as inputs for subsequent computations.However,the stepwise partitioning method of generating granular balls leads to instability in the results and a high time cost due to multiple iterations.This paper introduces the concept of natural neighbor clustering to study the generation of granular balls and proposes an adaptive granular ball generation algorithm based on local center search among natural neighbors.Benefiting from the parameter-free adaptiveness of the natural neighbor local center search algorithm,this method is insensitive to purity threshold parameters,making it a fully parameter-free,adaptive granular ball generation algorithm.Additionally,since this algorithm partitions the entire dataset in each iteration,it can generate more granular balls and reduce the number of iterations.Experimental validation shows that this algorithm offers speed advantages and higher stability under high purity thresholds or high noise levels compared to the granular ball generation algorithm,and it achieves classification accuracy comparable to that of the granular ball generation algorithm and KNN.