An improved global k-means clustering algorithm

K-means(KM) clustering algorithm is well known for its simplicity and efficiency. However, the clustering effect is greatly influenced by the selection of initial centers. To solve this problem, one of the improved algorithms is global k-means (GKM) which performs the clustering process in an incremental manner. This incremental manner makes GKM get rid of the influence of initial points selection and reach the global optimum or near global optimum results. However, GKM requires high computational cost. Therefore, an improved global k-means (IGKM) algorithm is proposed using a new guarantee reduction to reduce the computational load of GKM. Centroid theorem is introduced to reduce the computational time further. Simulation results on 14 datasets demonstrate that our IGKM algorithm can obtain better clustering results and requires less running time.