基于自适应模糊聚类的大数据价值分配研究

Research on Value Allocation of Big Data Based on Adaptive Fuzzy Clustering

研究旨在改进大数据环境下群体计算任务价值分配的准确性,通过对比随机分配算法与主题感知分配算法,提出了一种基于自适应模糊聚类的任务分配策略。结果显示,在多个数据集上,新策略的准确率显著高于随机分配算法,分别从52%提升到约78%、46%提升至80%,并保持了稳定的耗时。研究提出的基于自适应模糊聚类的大数据价值分配算法的分配效果,在任务规模增大时得到提升,显示出较好的可扩展性,在群体计算任务分配中,提供了更为合理和高效的解决方案。研究成果可以有效提高大数据分析计算任务的处理效率及准确率。

The study aims to improve the accuracy of group computing task value assignment in a big data environment,and proposes an adaptive fuzzy clustering-based task assignment strategy by comparing the random assignment algorithm with the topic-aware assignment algorithm.The results show that the accuracy of the new strategy is significantly higher than that of the random assignment algorithm on multiple datasets,from 52%to about 78%and 46%to 80%,respectively,and maintains a stable elapsed time.The allocation effect of the proposed adaptive fuzzy clustering-based big data value allocation algorithm is improved when the task size increases,shows better scalability,and provides a more reasonable and efficient solution in group computing task allocation.The research results can effectively improve the processing efficiency and accuracy of big data analysis and calculation tasks.