Semi-Supervised clustering aims to partition the data points into different clusters based on the user-specified must-link and cannot-link constraints. The current semi-supervised clustering algorithms either modify the clustering methods or combine the metric learning approaches to adapt the clustering result as consistent with the pairwise constraints as possible. However, few of them try to explicitly compute the degrees of influence that each pairwise constraint exerts on the unconstrained data points. This paper proposes a semi-supervised clustering algorithm via a two-level random walk, which is composed of a lower-level random walk on vertices and a higher-level random walk on components. The lower-level random walk is responsible for computing the influence range of every vertex constrained by a pairwise constraint. This information is encapsulated in an intermediate structure called “component”. The higher-level random walk further propagates the pairwise constraints on the components with adaptive strength, followed by the integration of all the constraint influence into a cluster indicating matrix. The experiments on UCI database and large real-world data sets demonstrate that, compared with other semi-supervised clustering algorithms, the proposed method not only produces more satisfactory clustering results but also exhibits good efficiency.%半监督聚类旨在根据用户给出的必连和不连约束，把所有数据点划分到不同的簇中，从而获得更准确、更加符合用户要求的聚类结果.目前的半监督聚类算法大多数通过修改已有的聚类算法或者结合度规学习，使聚类结果与点对约束尽可能地保持一致，却很少考虑点对约束对周围无约束数据的显式影响程度.提出一种由在顶点上的低层随机游走和在组件上的高层随机游走两部分构成的双层随机游走半监督聚类算法，其中，低层随机游走主要负责计算选出的约束顶点对其他顶点的影响范围和影响程度，称为组件；高层随机游走则进一步将各个点对约束以自适应的强度在组件上进行约束传播，把它们在每个顶点上的影响综合在一个簇指示矩阵中.UCI 数据集和大型真实数据集上的实验结果表明，双层随机游走半监督聚类算法比其他半监督聚类算法更准确，也比较高效.
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