Abstract: Focusing on the problem of inadequate use of priori knowledge and the problem that the number of clusters is required in most existing algorithms in network fault detection, a new semi-supervised automatic clustering algorithm that combines propagating pairwise constraints information and determining the number of clusters automatically is proposed. By learning a new similarity measure function to satisfy the constraints, and improving the NJW algorithm, automatic spectral clustering is done on the non-standardized Laplacian matrix eigenvector to improve the clustering performance. The experiments based on the UCI standard data sets and network measured data sets show that the proposed algorithm is more accurate in clustering than the comparative algorithms, and can meet the actual needs of the network fault detection.

Key words: semi-supervised clustering, spectral clustering, pairwise constraints, similarity matrix, automatic clustering, network fault detection

摘要： 针对网络故障检测中利用先验知识不足和多数谱聚类算法需事先确定聚类数的问题，提出一种新的基于成对约束信息传播与自动确定聚类数相结合的半监督自动谱聚类算法。通过学习一种新的相似性测度函数来满足约束条件，改进NJW聚类算法，对非规范化的Laplacian矩阵特征向量进行自动谱聚类，从而提高聚类性能。在UCI标准数据集和网络实测数据上的实验表明，该算法较相关比对算法聚类准确率更高，可满足网络故障检测的实际需要。

关键词: 半监督聚类, 谱聚类, 成对约束, 相似度矩阵, 自动聚类, 网络故障检测