Unsupervised Clustering Using Graph Transduction

We present a graph-based iterative algorithm for clustering task. The existing literatures in this domain often use the distance measure between the testing data point individual which is proved not enough in the real applications. In this paper, we think about the core concept in semi-supervised learning method, and use a graph to reflect the original distance measure, and combine the density information of the data distribution with the distance measure. Given a set of testing data, we select the original data randomly and use graph transduction iterative on the defined graph. The given algorithm is rapid and steady comparing with the existing clustering method. The experiments show that the novel algorithm is effective for the clustering task.