Abstract: To address the challenges of high computational resource consumption and low clustering efficiency in big data clustering scenarios,researchers have proposed an innovative distributed two-stage clustering method based on node sampling.This method first performs local clustering operations on the data at each local node,and then extracts representative data samples from each node based on the results of the local clustering.The selected sample data from each node is then transmitted to the central node.At the central node,further clustering analysis is conducted on the aggregated sample data,and the results of the sample clustering are transmitted back to the local nodes.Finally,each local node combines its own local clustering results with the sample clustering results to complete the final unification of clustering labels.Through this process,the two-stage clustering method has transformed the traditional centralized clustering algorithms into a more scalable,distributed model,and ensures the consistency of the clustering result to the global dataset.Theoretical analysis and experimental results both indicate that compared with the conventional full-data centralized clustering techniques,the two-stage clustering method offers a framework which effectively integrates the efficiency of parallel processing and the accuracy of integrated analysis.Without sacrificing the accuracy of clustering,it significantly improves clustering efficiency and reduces time costs,which provided a feasible and robust distributed solution tailored for the complexities inherent in big data clustering tasks.

Key words: Big data clustering, Distributed computing, Node sampling, Parallel computing, Two-stage clustering