Proposing a New Algorithm to Detect Local Outliers in Data Stream

Data streams outlier mining is an important and active research issue in anomaly detection. Outliers are large deviate from others data points. They are often not the errors, and may carry important information. Recently, many studies on outlier detection in the database are done. Many algorithms have been proposed to detect outliers, but most of them are effective on static data. As data streams evolve during the time, traditional methods cannot perform well on them. These algorithms often can lead us to a wrong decision. The false positive rate of the algorithms will be high. In this paper, an algorithm is proposed to divide the streams to pieces evenly and compute local outlier factor for every data. The proposed algorithm uses a list as candidate list for the outliers. The proposed algorithm detects outliers and unusual patterns by postponing at outlier detection. The experimental results on synthetic and real datasets show that the proposed algorithm was successful in reducing false positive rate and increasing its accuracy.