Statistical Anomaly Detection Technique for Real Time Datasets

International Journal of Computer Trends and Technology (IJCTT)          
© - December Issue 2013 by IJCTT Journal
Volume-6 Issue-2                           
Year of Publication : 2013
Authors :Y.A.Siva Prasad , Dr.G.Rama Krishna


Y.A.Siva Prasad , Dr.G.Rama Krishna"Statistical Anomaly Detection Technique for Real Time Datasets"International Journal of Computer Trends and Technology (IJCTT),V6(2):89-94 December Issue 2013 .ISSN Published by Seventh Sense Research Group.

Abstract:- -Data mining is the technique of discovering patterns among data to analyze patterns or decision making predictions. Anomaly detection is the technique of identifying occurrences that deviate immensely from the large amount of data samples. Advances in computing generates large amount of data from different sources, which is very difficult to apply machine learning techniques due to existence of anomalies in the data. Among data mining techniques, anomaly detection plays an important role. The identified rules or patterns from the data mining techniques can be utilized for scientific discovery, business decision making, or future prediction. Several algorithms has been proposed to solve problems in anomaly detection, usually these problems are solved using a distance metric, data mining techniques, statistical techniques etc. But existing algorithms doesn’t give optimal solution to detect anomaly objects in the heterogeneous datasets. This paper presents statistical control chart approach to solve anomaly detection problem in continuous datasets. Experimental results shows that proposed approach give better results on continuous datasets but doesn’t perform well in heterogeneous datasets.


[1] M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, “LOF: Identifying densitybased local outliers,” In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, pp. 93–104, 2000.
[2] Y. Tao and D. Pi, “Unifying density-based clustering and outlier detection,” 2009 Second International Workshop on Knowledge Discovery and Data Mining, Paris, France, pp. 644–647, 2009.
[3] E. M. Knorr and R. T. Ng. Algorithms for mining distance-based outliers in large datasets. In VLDB, 1998.
[4] Pavel Berkhin, “Survey of Clustering Data Mining Techniques”, 2survey.html
[5] K. P. Chan and A.W. C. Fu, “Efficient time series matching by wavelets,” In Proceeding ICDE ’99 Proceedings of the 15th International Conference on Data Engineering, Sydney, Austrialia, March 23-26, 1999, p. 126, 1999..
[6] V. Chandola, A. Banerjee, and V. Kumar, “Anomaly detection: A survey,” ACM Computing Surveys, vol. 41, no. 3, p. ARTICLE 15, July 2009.
[7] R. Fujimaki, T. Yairi, and K. Machida, “An anomaly detection method for spacecraft using relevance vector,” in Learning, The Ninth Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD). Springer, 2005, pp. 785–790.

Keywords:-Outlier, Data Mining,Patterns