Abstract: Fuzzy C-means?is an important?soft-clustering algorithm, but with the increased amount of data the time complexity will be increased. In this paper, a parallel?fuzzy?C-means?algorithm based on?the MapReduce is proposed. The fuzzy?C-means?algorithm is redesigned to meet the MapReduce programming model. The membership degree of data set to the center is computed in parallel, and the new cluster center is re-calculated, so that the higher calculating efficiency of processing large amount of data can be got. The experimental results show that the parallel?fuzzy?C-means?algorithm based on?the MapReduce has the advantages of both high speedup and good scalability.

Key words: fuzzy C-means, parallel computing, MapReduce, data mining, cloud computing

摘要： 模糊C均值是一种重要的软聚类算法，针对模糊C均值的随着数据量的增加，时间复杂度过高的缺点，提出了一种基于MapReduce的并行模糊C均值算法。算法重新设计模糊C均值，使其符合MapReduce的基于key/value的编程模型，并行计算数据集到中心点的隶属度，并重新计算出新的聚类中心，提高了模糊C均值处理大容量数据的计算效率。实验结果表明，基于MapReduce的并行模糊C均值算法具有较高的加速比和扩展性。

关键词: 模糊C均值, 并行计算, MapReduce编程模型, 数据挖掘, 云计算