计算机工程与应用2016，Vol.52Issue(1)：66-70,5.DOI:10.3778/j.issn.1002-8331.1504-0199
基于MapReduce的并行SFLA-FCM聚类算法
Parallel SFLA-FCM clustering algorithm based on MapReduce
苟杰 1马自堂1
作者信息
- 1. 解放军信息工程大学三院,郑州 450000
- 折叠
摘要
Abstract
Fuzzy C-Means(FCM)algorithm is a kind of widely used clustering algorithm. But the clustering quality of the FCM depends on the choice of initial values. Combined with the better searching performance of the Shuffled Frog Leaping Algorithm(SFLA), this paper presents a parallel SFLA-FCM clustering algorithm based on MapReduce. The algorithm uses the information transmitting within subgroups and global information exchange to search the high quality of the clustering center. The algorithm process is designed to conform to the MapReduce programming model and it has the ability of dealing with large-scale dataset. The experiments prove that parallel SFLA-FCM improves the searching performance and the accuracy of clustering results and has high speedup and scalability.关键词
聚类/模糊C均值算法/混合蛙跳算法/MapReduceKey words
clustering/Fuzzy C-Means(FCM)/Shuffled Frog Leaping Algorithm(SFLA)/MapReduce分类
信息技术与安全科学引用本文复制引用
苟杰,马自堂..基于MapReduce的并行SFLA-FCM聚类算法[J].计算机工程与应用,2016,52(1):66-70,5.
