Indicative Support Vector Clustering with Its Application on Anomaly Detection

In many learning scenarios, supervised learning is hardly applicable due to the unavailability of a complete set of data labels, while unsupervised model overlooks valuable user feedback in an interactive system setting. In this paper, a novel semi-supervised support vector clustering algorithm is presented, where a small number of user indicated labels are available as supervised information. We apply the clustering algorithm in the anomaly detection area, and show that the given labels significantly improve the recognition of anomalies. Moreover, the partially labeled data proliferates the information without extra computation but strengthening the robustness to anomalies.