An efficient global K-means clustering algorithm based on weighted space partitioning
Aiming at the problem of large amount of calculation caused by exhaustive sample points in global K-means clustering algorithm,this paper proposes an efficient global K-means clustering algorithm based on weighted space partition.Firstly,the sample space is divided into grids,and then the density criterion and distance criterion are proposed to filter the grids,and the grids with large density and far distance from each other are retained as candidate center grids.In order to avoid the limitation that the global K-means algorithm only selects candidate centers in the sample set,the weight criterion and the center iteration strategy are proposed to expand the candidate centers and increase the diversity of the candidate centers.Finally,the candidate centers were traversed by incremental clustering to obtain the final clustering result.The experimental results on UCI data sets show that compared with the global K-means algorithm,the computational efficiency of the new algorithm is improved by 89.39%～95.79%on average under the premise of ensuring the clustering accuracy.Compared with K-means++,IK-+and the recently proposed CD algorithm,the new algorithm has higher accuracy and overcomes the problem of unstable clustering results caused by random initialization.
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