Continuous Outlier Detection Based on Sliding window on Continuous Data Streams

Anomaly detection is considered an important data mining at the discovery of element (also known as outliers) that slow significant diversion from the expected case. This paper studies the problem of outlier detection on continuous data streams. The proposed system MCOD(Micro –Cluster-Based Continuous Outlier Detection)algorithms for continuous outlier monitoring on deterministic data streams based on the sliding window .In this paper ,we design efficient algorithms for continuous monitoring of distance-based outliers, in sliding windows over data streams, aiming at the elimination of the limitations of previously proposed SVDD algorithms. Our primary concerns are efficiency improvement and storage consumption reduction. The proposed algorithms are based on an event-based framework that takes advantage of the expiration time of objects to avoid unnecessary computations. The MCOD algorithm is an outlier detection method based on micro-cluster. This technique is able to reduce the required storage overhead ,run faster than previously proposed SVDD technique and offers significant flexibility. Experiments performed on real-life as well as synthetic data sets.