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 (SVM)-V-3.