Semi-supervised spectral clustering using shared nearest neighbor for data with different shape and density

Abstract

In the absence of supervisory information in spectral clustering algorithms, it is difficult to construct suitable similarity graphs for data with complex shapes and varying densities. To address this issue, this paper proposes a semisupervised spectral clustering algorithm based on shared nearest neighbor (SNN). The proposed algorithm combines the idea of semi-supervised clustering, adding SNN to the calculation of the distance matrix, and using pairwise constraint information to find the relationship between two data points, while providing a portion of supervised information. Comparative experiments were conducted on artificial data sets and University of California Irvine machine learning repository datasets. The experimental results show that the proposed algorithm achieves better clustering results compared to traditional K-means and spectral clustering algorithms.