关键词: 谱聚类, 协方差矩阵, 相似矩阵

Abstract: For the traditional spectral clustering algorithm does not solve the cluster division process, the cross-cluster cross-region sample points have an impact on the clustering effect. In this paper, a spectral clustering algorithm based on local covariance is proposed. The algorithm mainly introduces a new method for calculating the similarity affinity matrix between samples. Firstly, it divides the child by calculating the Euclidean distance between sample points. Then it calculates the covariance matrix of the small subset, sets the threshold to eliminate the intersection, the remaining point constructs a similarity matrix, and then the eigenvalue decomposition of the similarity matrix is done, and finally it uses the classical [k]-means algorithm for the eigenvectors matrix clustering. Experiments on real data sets such as Control show that the algorithm of this paper obtains better results in terms of clustering accuracy, standard mutual information and other indicators.

Key words: spectral clustering, covariance matrix, similarity matrix