Spectral Clustering Algorithm for Large Scale Data Set Based on Accelerating Iterative Method
一种基于加速迭代的大数据集谱聚类方法

The advantage of the traditional spectral clustering algorithm is applicable in the small scale data set. A new method was proposed in the light of the laplacian matrix characteristics. First, a new Gram matrix was reconstructed and some lies of the new matrix were needed, then the eigen-decomposition based on accelerating iterative method was solved. The calculation speed of the proposed method is very fast and the space complexity is small for large scale data set