Earticle

The kmeans algorithm is an unsupervised classification algorithm. It has some drawbacks, the number of classes has to be known a priori, the initialization phase and the local optimums. We present in this paper an improvement based on evolutionary strategies and on the Xie and Beni criterion in order to get around these three difficulties. We design a new evolutionist kmeans algorithm. We suggest a new mutation operator that allows the algorithm to avoid local solutions and to converge to the global solution in a small computation time. We have optimized the Xie and Beni criterion by evolutionary strategies for the optimal choice of the number of classes. The proposed method is validated on several simulation examples. The experimental results obtained show the rapid convergence and the good performances of this new approach.

With development of 3D scanner, it becomes more convenient to access point data. However, for processing the large-scale point cloud, it raised a new challenge for computer graphics. This paper places an emphasis on the point data own characteristics, and then the point data have been divided into certain point sets by clustering algorithm, that is will be divided into different clusters. In order to suit for the point data organization or space division, the clustering algorithm would be improved. This paper provided a new Kmeans algorithm with density constraints. Before processing the point cloud by Kmeans algorithm with density constraints, the density of the point cloud have been defined in this paper, the density of the point cloud can be used for quantification of the convergence. Finally, the Kmeans algorithm with density constraints is verified by the experiment results. Our experiment showed that the improved Kmeans can reduce the processing time, especially, As the increase of the value of K, that is number of cluster, the calculating time of the clustering algorithm can be decreased greatly. In addition, with the increases of the the scale of data size, the stability of the improved Kmeans algorithm has been verified.