Parallel fuzzy C-means algorithm based on MapReduce

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.