关键词: 半监督聚类, 谱聚类, 共享近邻, 成对约束

Abstract: The spectral clustering algorithm is a machine learning algorithm based on the theory of spectral partitioning. It can cluster on any shape of the sample space and converge to the global optimal solution. However, the traditional spectral clustering algorithm is difficult to find out the large density difference clusters, the choice of parameters depends on multiple tests and personal experience. Combined with the idea of semi-supervised clustering, a pair of constrained spectral clustering algorithm based on shared neighbors（PCSC-SN） is proposed under the premise of giving some supervisory information. The PCSC-SN algorithm uses a shared neighbor to measure the similarity between data pairs, and uses the active constraint information to find the relationship between two data points. A series of experiments are done on the data set UCI. The experimental results show that this algorithm can obtain better clustering effect compared with the traditional clustering algorithm.

Key words: semi-supervised clustering, spectral clustering, shared neighbors, paired constraints