计算机应用研究2018，Vol.35Issue(3)：675-679,5.DOI:10.3969/j.issn.1001-3695.2018.03.008
自适应布谷鸟搜索的并行K-means聚类算法
Parallel K-means clustering algorithm based on adaptive cuckoo search
摘要
Abstract
The original K-means clustering algorithm is seriously affected by initial centroids of clustering and easy to fall into local optima.So this paper proposed an improved K-means clustering based on adaptive cuckoo search,and achieved the parallelization of the improved algorithm using MapReduce programming model.It implemented accuracy experiments and efficiency experiments 10 times respectively on Hadoop platform for every different data sets,the experimental results show that:a)compared with the original K-means algorithm and PSO-Kmeans,the average accuracy of clustering improved in the experiments which test on four UCI standard data sets;b)tested the average execution efficiency of clustering in the experiments which test on five random incremental data sets,when the amount of data was very large,significantly better than original K-means algorithm,slightly better than PSO-Kmeans.It can be concluded that the algorithm can be applied to large data clustering,and will play a significant effect.关键词
聚类/K-均值算法/布谷乌搜索算法/Hadoop/MapReduceKey words
clustering/K-means algorithm/cuckoo search algorithm/Hadoop/MapReduce分类
信息技术与安全科学引用本文复制引用
王波,余相君..自适应布谷鸟搜索的并行K-means聚类算法[J].计算机应用研究,2018,35(3):675-679,5.基金项目
国家科技重大专项资助项目(2012ZX07-307-002) （2012ZX07-307-002）
