Clustering algorithm analysis, including time and space complexity analysis, has always been discussed in the literature. The emergence of big data has also created a lot of challenges for this issue. Because of high complexity and execution time, traditional clustering techniques cannot be used for such an amount of data. This problem has been addressed in this research. To present the clustering algorithm using a bee colony algorithm and high-speed read/write performance, Map-Reduce architecture is used. Using this architecture allows the proposed method to cluster any volume of data, and there is no limit to the amount of data. The presented algorithm has good performance and high precision. The simulation results on 3 datasets show that the presented algorithm is more efficient than other big data clustering methods. Also, the results of our algorithm execution time on huge datasets are much better than other big data clustering approaches.

聚类算法分析，包括时间和空间复杂性分析，一直在文献中进行讨论。大数据的出现也为这个问题带来了很多挑战。由于高复杂度和执行时间，传统的群集技术无法用于如此大量的数据。这个研究已经解决了这个问题。为了展示使用蜂群算法和高速读写性能的聚类算法，使用了Map-Reduce体系结构。使用这种体系结构可以使所提出的方法对任意数量的数据进行聚类，并且对数据量没有限制。该算法具有良好的性能和较高的精度。在3个数据集上的仿真结果表明，所提出的算法比其他大数据聚类方法更有效。还，