Anomaly Detection Using Data Mining Methods

International Journal of Computer Trends and Technology (IJCTT)          
© 2019 by IJCTT Journal
Volume-67 Issue-12
Year of Publication : 2019
Authors : S.Sreekanth, Dr. P.C.Rao


MLA Style:S.Sreekanth, Dr. P.C.Rao  "Anomaly Detection Using Data Mining Methods" International Journal of Computer Trends and Technology 67.12 (2019):20-23.

APA Style S.Sreekanth, Dr. P.C.Rao. Anomaly Detection Using Data Mining Methods International Journal of Computer Trends and Technology, 67(12),20-23.

Anomaly is characterized as an occasion that veers off a lot from different occasions. The recognizable proof of anomaly can prompt the disclosure of valuable and important Data. Anomaly implies it's occur eventually it's not customary movement. Research about Detection of Outlier has been widely ponders in the previous decade. In any case, most existing examination concentrated on the calculation dependent on explicit Data, contrasted and anomaly discovery approach is as yet uncommon. In this paper fundamentally centered around various sort of exception identification approaches and thinks about it's inclined and cones. In this paper we primarily disperse of exception recognition approach in two sections exemplary anomaly approach and spatial exception approach. The old style exception approach recognizes anomaly in genuine exchange dataset, which can be assembled into measurable methodology, separation approach, deviation approach, and thickness approach. The spatial exception approach identify anomaly dependent on spatial dataset are not quite the same as exchange Data, which can be classified into divided methodology and chart approach. At long last, the correlation of anomaly discovery draws near.

[1] Agarwal, D., Phillips, J.M., Venkatasubramanian, “The hunting of the bump: on maximizing statistical discrepancy”. In: Proc. th Ann. ACM-SIAM Symp. On Disc. Alg. pp. 1137–1146 (06).
[2] Y. Kou, C.-T. Lu, and D. Chen. “Spatial weighted outlier detection”. In Proceedings of the Sixth SIAM International Conference on Data Mining,pp. 614–6, Bethesda, Maryland, USA, 06.
[3] Aggarwal, C. C., Yu, S. P., “An effective and efficient algorithm for high-dimensional outlier detection, The VLDB Journal, 05, vol. 14, pp. 1-2.
[4] Lazarevic, A., Kumar” Feature Bagging for Outlier Detection”. In: KDD (05).
[5] N. R. Adam, V. P. Janeja, and V. Atluri., “Neighborhoodbased detection of anomalies in high - dimensional spatiotemporal sensor datasets”. In Proceedings of the 04 ACM symposium on Applied computing, Nicosia, Cyprus, 04. pp. 576–583
[6] S. C. Shashi Shekhar, “Spatial Databases: A Tour. Prentice Hall”, 03.
[7] Papadimitriou, S., Kitawaga, H., Gibbons, P., Faloutsos, C., “LOCI: Fast outlier detection using the local correlation integral”, Proc. of the Int’l Conf. on Data Engineering, 03.
[8] Chang-Tien Lu, Dechang Chen,Yufeng Kou, “Detecting spatial outliers with multiple attributes”, Tools with Artificial Intelligence, 03. Proceedings. 03, pp.1–128.
[9] Yu, D., Sheikholeslami, G. and Zang, “A find out: finding outliers in very large datasets”. In Knowledge and Information Systems, 02, pp.387-412.
[10] Jin, W., Tung, A.K.H., Han, J.W. “Mining Top-n Local Outliers in Large Databases”. In: KDD (01).
[11] H. Liu, K. C. Jezek, and M. E. O’Kelly, “Detecting outliers in irregularly distributed spatial data sets by locally adaptive and robust statistical analysis and gis”. International Journal of Geographical Information Science,15(8), 01. pp.7–741
[12] Aggarwal, C.C, Yu, P. "Outlier detection for high dimensional data", Proceedings of the ACM SIGMOD International Conference on Management of Data. Santa Barbara, CA, 01, pp. 37-47.
[13] Breunig, M.M., Kriegel, H.P., and Ng, R.T., “LOF: Identifying density-based local outliers.” ACM Conference Proceedings, 00, pp. 93-104.
[14] E. Knorr, R. Ng, and V. Tucakov, “Distance-Based Outlier: Algorithms and Applications,” VLDB J., vol. 8, nos. 3-4 00, pp. 7-3.
[15] S. Ramaswamy, R. Rastogi, and K. Shim, “Efficient Algorithms for Mining Outliers from Large Data Sets,”Proc. Int’l Conf.

Anomaly detection; spatial data, transaction data.