关键词: 无监督学习, 多视角聚类, 相似矩阵, 谱聚类, 共享近邻

Abstract: In order to solve the problem that the construction of the similarity matrix in the spectral clustering algorithm cannot meet the higher similarity of the data points within the cluster, a Multi-View spectral clustering algorithm based on Shared Nearest Neighbor (MV-SNN) was given. Firstly, the similarity between two data points with a large number of shared neighbors was increased, making the similarity between the data points in the same cluster higher. Then, the improved similarity matrices of multiple views were integrated to obtain a global similarity matrix. Finally, considering that the general spectral clustering methods still need k-means clustering algorithm to divide the data points at the later stage, a rank constraint method of Laplacian matrix was proposed to directly obtain the final cluster structure through the global similarity matrix. Experimental results show that compared with other multi-view spectral algorithms, MV-SNN algorithm has the three measurement standards of clustering:accuracy, purity and normalized mutual information improved by 1%-20%, and the clustering time reduced by about 50%. It can be seen that MV-SNN algorithm can improve the clustering performance and reduce the clustering time.

Key words: unsupervised learning, multi-view clustering, similarity matrix, spectral clustering, shared neighbor