The spectral clustering algorithm is widely used in many fields because of its advantages of identifying the nonconvex data distribution, and effectively avoiding the local optimal solution without the dimension limitation of data points. However, with the growth of the amount and dimension of the data, it is very necessary to reduce the algorithm’s computation time on the premise of guaranteeing the clustering accuracy. Moreover, besides the data set itself, the factors affecting the clustering quality of the spectral clustering algorithm include the method of solving distance matrix, the scale parameters of similarity matrix and the form of Laplacian matrix. Aiming at the problems mentioned above, we apply the message passing interface (MPI) parallel programming model to the spectral clustering algorithm. The tnearest neighbor method is then used in the transformation of Laplacian matrix approximation in the spectral clustering algorithm. Meanwhile, we select the local scaling parameter as the selftuning scaling parameter in the algorithm. Based on the above analysis, we propose a parallel implementation of the sparse local scaling parallel spectral clustering (SLSPSC), then conduct experiments on four different data sets, and analyze and compare the results with those of the current parallel spectral clustering (PSC) in running time and clustering quality. Experimental results show that the total computation time of the SLSPSC is greatly reduced when calculating the Laplacian matrix, and the quality of some data sets is improved.
