Radius-Distance Based Semi-Supervised Algorithm

In order to improve the accuracy and efficiency of directly use of the k-means clustering for semi-supervised learning, the paper proposes a new semi-supervised learning algorithm based on radius-distance. In the algorithm, according to radius, farthest distance of sample to the cluster center of unlabelled samples using k-means, and distance, from cluster center of unlabelled samples to center of labeled samples, a small amount of unlabeled data are selected to aid training learning. Experimental results on the Kddcup´99 demonstrate that the advantages of proposed algorithm over the k-means method and S3VM.