The Study of Parallel K-Means Algorithm

Abstract

Clustering analysis plays an important role in scientific research and commercial application. K-means algorithm is a widely used partition method in clustering. As the dataset's scale increases rapidly, it is difficult to use K-means to deal with massive amount of data. A parallel strategy is incorporated into clustering method and a parallel K-means algorithm is proposed. For enhancing the efficiency of parallel K-means, dynamic load balance is introduced. Data parallel strategy and Master/Slave model are adopted. The experiments demonstrate that the parallel K-means has higher efficiency and universal use.