DETECTING OUTLIERS USING osPCA

Anomaly detection has been an important research topic in data mining and machine learning. The most anomaly detection method s are typically implemented in batch mode so that it cannot be easily extended to large-scale problems without sacrificing computation and memory specification. An online oversampling principal component analysis (osPCA) algorithm is implemented to address the problem, then aiming at detecting the presence of outliers from a large amount of data via an online updating technique and at the same time it sends notification message to user’s mail id. The proposed framework is favored for online applications which have computation or memory restrictions. By checking with the well-known power method for PCA and other popular anomaly detection algorithms, the results verify the feasibility of our proposed method in terms of both accuracy and efficiency