基于改进人工蜂群算法与MapReduce的大数据聚类算法

针对大数据聚类算法计算效率与聚类性能较低的问题，提出了一种基于改进人工蜂群算法与MapReduce的大数据聚类算法。将灰狼优化算法与人工蜂群算法结合，同时提高人工蜂群算法的搜索能力与开发能力，该策略能够有效地提高聚类处理的性能；采用混沌映射与反向学习作为ABC种群的初始化策略，提高搜索的解质量；将聚类算法基于Hadoop的MapReduce编程模型实现，通过最小化类内距离的平方和实现对大数据的聚类处理。实验结果表明，该算法有效地提高了大数据集的聚类质量，同时加快了聚类速度。

Clustering algorithm of big data based on improved artificial bee colony algorithm and MapReduce

Aiming at the problems of low computational efficiency and low clustering performance of clustering algorithms for big data, this paper presented a clustering algorithm of big data based on the improved artificial bee colony(ABC) algorithm and MapReduce. This algorithm combined the grey wolf optimizer algorithm and ABC algorithm, and improved the exploration and exploitation of the ABC algorithm simultaneously, it could help to improve the clustering performance effectively. The algorithm utilized the chaotic map and backward learning as the initial strategy of ABC colony to improve the solution quality of search procedure. It realized the clustering algorithm based on MapReduce programming model, and realized the clustering process for big data by minimizing the quadratic sum of inner class distances. Experimental results demonstrate that the proposed algorithm improves the clustering quality of big data, and speedups the clustering procedure.