关键词: 聚类, 模糊C均值算法, 混合蛙跳算法, MapReduce

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.

Key words: clustering, Fuzzy C-Means（FCM）, Shuffled Frog Leaping Algorithm（SFLA）, MapReduce