Online Anomaly Detection under Over-sampling PCA

Anomaly detection is the process of identifying unusual behavior. Outlier detection is an important issue in data mining and has been studied in different research areas. In this paper we use Leave One Out procedure to check each individual point the with or without effect on the variation of principal directions. Based on this idea, an over-sampling principal component analysis (osPCA) outlier detection method is proposed for emphasizing the influence of an abnormal instance. Except for identifying the suspicious outliers, we also design an online anomaly detection to detect the new arriving anomaly. In addition, we also study the quick updating of the principal directions for the effective computation and satisfying the online detecting demand. It is widely used in data mining; the proposed framework is favored for online applications which have computation or memory limitations. Compared with the all existing algorithms, our proposed method is in terms of flexibility, accuracy and efficiency.