Fast K-means Clustering Point Cloud Reduction Algorithm for Custom Clustering Center Points
An improved clustering algorithm of GK-means is proposed to simplify point cloud data in this paper.The al-gorithm first stipulates the value K as the limit of the final number of clusters,and then improves the point selection strate-gy,selects the cluster center with the farthest point sampling,subdivides the cluster,calculates the Euclidean distance from the point to the cluster center,obtains the position of the minimum value,and puts it into the cluster where the minimum distance is located.The experimental results show that the improved K-means algorithm can improve the success probabili-ty of the algorithm and run faster.When the point cloud is simplified,the feature region can retain the detailed features of the point cloud model completely,and the reconstructed results have high smoothness.
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