Fuzzy C-means clustering integration algorithm is a method to improve clustering quality by using integration ideas, but as the amount of data increases, its time complexity increases. A parallel FCM clustering integration algorithm based on MapReduce is proposed. The algorithm uses a random initial clustering centre to obtain differentiated cluster members. By establishing an overlapping matrix between clusters, the clustering labels are unified to find logical equivalence clusters. The cluster members share the classification information of the data objects by voting to obtain the final clustering result. The experimental results show that the parallel FCM clustering integration algorithm has good performance, and has high speedup and good scalability.

模糊C-均值聚类集成算法是一种利用集成思想提高聚类质量的方法，但是随着数据量的增加，其时间复杂度也随之增加。提出了一种基于MapReduce的并行FCM聚类集成算法。该算法使用随机初始聚类中心来获得差异化的聚类成员。通过在聚类之间建立重叠矩阵，可以将聚类标签统一起来以找到逻辑等价聚类。聚类成员通过投票共享数据对象的分类信息以获得最终聚类结果。实验结果表明，并行FCM聚类集成算法具有良好的性能，并且具有较高的速度和良好的可扩展性。