Improved large data spectral clustering algorithm based on sampling subspace constraint

Abstract

On the basis of analyzing the equivalent function of the objective function of classical spectral clustering algorithm and the weighted kernel k-means objective function,an improved large-scale data spectrum clustring algorithm based on sampling subspace constraint was designed,the weighted kernel k-means iterative optimization was used to avoid the large resource consumption of Laplacian matrix feature decomposition,and by using data sampling and constraining the cluster center to the subspace generated by the sampling points,the use of all kernel matrices was avoided,thereby reducing the time-space complexity of classical algorithms.Theoretical analysis and experimental results show that the improved algorithm can greatly improve the clustering efficiency on the basis of maintaining similar clustering accuracy with the classic algorithm and verify the effectiveness of the proposed algorithm.

Keywords

• large scale data spectral clustering
• weighted kernel k-means algorithm
• data sampling
• matrix feature decomposition
• kernel matrix