Volume : IV, Issue : VI, June - 2015

Continuous Outlier Detection Based on Sliding window on Continuous Data Streams

Shimna T, S. Kavitha Murugesan

Abstract :

 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. ?is paper studies the problem of outlier detection on continuous data streams. ?e 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. ?e proposed algorithms are based on an event–based framework that takes advantage of the expiration time of objects to avoid unnecessary computations. ?e MCOD algorithm is an outlier detection method based on micro–cluster. ?is 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.

Keywords :

Article: Download PDF   DOI : 10.36106/ijsr  

Cite This Article:

SHIMNA T, S.KAVITHA MURUGESAN Continuous Outlier Detection Based on Sliding window on Continuous Data Streams International Journal of Scientific Research, Vol : 4, Issue : 6 June 2015

Number of Downloads : 969

References :