一种基于MapReduce的改进人工蜂群算法  

A Artificial Bee Colony for Big Data Clustering Based on MapReduce

作　　者：王凯杰 

机构地区：[1]上海先觉商贸有限公司,上海200331

出　　处：《软件导刊》2018年第2期71-73,共3页Software Guide

摘　　要：针对现有序列聚类算法在对大规模数据进行聚类时,内存空间和计算时间开销较大的问题,提出了基于MapReduce的人工蜂群聚类算法。该算法通过引入MapReduce并行编程范式,快速计算聚类中心适应度,可实现对大规模数据的高效聚类。基于仿真数据对算法的聚类效果和聚类效率进行了验证。实验结果表明,与现有PK-Means算法和并行K-PSO算法相比,该算法具有更好的聚类效果和更高的聚类效率。For the problem that the computational cost of most sequential clustering algorithms is expensive in terms of memory space and the time complexities,an improved artificial bee colony based on MapReduce for large-scale data clustering is proposed.The MapReduce programming paradigm is introduced in this algorithm to calculate the fitness.The quality and efficiency of the proposed algorithm were test by using the synthetic dataset.Experimental results show that this algorithm performs better in clustering effect,scalability and computational efficiency compared with PK-Means and parallel K-PSO.

关 键 词：大数据 人工蜂群 聚类 并行编程范式 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]