A Review on Various Outlier Detection Techniques

Authors

  • Ashwini Jadhav  Computer Department Savitribai Phule Pune University, Nashik, Maharashtra, India
  • Kalpana Metre  Computer Department, Savitribai Phule Pune University, Nashik, Maharashtra, India

Keywords:

Outlier Detection, Anomaly Detection, Distance Based Outlier, Local Outlier, Global Outlier, Memory Efficiency

Abstract

Outlier detection or anomaly detection is important branch data mining. It monitors the data and extracts the unusual events from dataset. The outlier detection technique can be applied in variety of domains such as detection of intrusion , fraud analysis, human gait analysis etc. The outlier detection strategies vary with respect to the application requirement. The outlier can be extracted from static dataset as well as from continuously streaming data. This work aims to study various outlier detection strategies and its limitation. After analysis of existing techniques, a streaming based local outlier detection technique is proposed.

References

  1. Mahsa Salehi , Christopher Leckie , James C. Bezdek , Tharshan Vaithianathan and Xuyun Zhang, "Fast Memory Efficient Local Outlier Detection in Data Streams,"IEEE Transactions on Knowledge and Data Engineering, vol. 28, no. 12, pp. 3246 -3260, 2016.
  2. S Sadik and L. Gruenwald, "Research issues in outlier detection for data streams,"ACM SIGKDD Explorations Newsletter, vol. 15, no. 1, pp. 3340, 2014.
  3. V. Chandola, A. Banerjee, and V. Kumar, "Anomaly detection: Asurvey," ACM Computing Surveys, vol. 41, no. 3, pp. 158, 2009.
  4. S Rajasegarar, C. Leckie, and M. Palaniswami, "Anomaly detection in wireless sensor networks," IEEE Wireless Communications, vol. 15, no. 4, pp. 3440, 2008.
  5. D. Pokrajac, A. Lazarevic, and L. J. Latecki, "Incremental local outlier detection for data streams," in Computational Intelligence and Data Mining, 2007, pp. 504515.
  6. M Gupta, J. Gao, C. C. Aggarwal, and J. Han, "Outlier Detection for Temporal Data: A Survey," IEEE Transactions on Knowledge and Data Engineering, vol. 25,no. 1, pp. 120, 2013.
  7. C C. Aggarwal, "Outlier Analysis," 2013.
  8. K Yamanishi, J.-I. Takeuchi, G. Williams, and P. Milne, "Online unsupervised outlier detection using _nite mixtures with discounting learning algorithms," in SIGKDD,2000, pp. 320324.
  9. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu, "A framework for clustering evolving data streams," in VLDB, 2003, pp. 8192.
  10. F. Cao, M. Ester, W. Qian, and A. Zhou, "Density-based clustering over an evolving data stream with noise," in SIAM Conference on Data Mining, 2006, pp. 328339.
  11. C. C. Aggarwal, "A segment-based framework for modeling and mining data streams," Knowledge and information systems, vol. 30, no. 1, pp. 129, 2012.
  12. I. Assent, P. Kranen, C. Baldauf, and T. Seidl, "Anyout: Anytime outlier detection on streaming data," in Database Systems for Advanced Applications, 2012, pp.228242.
  13. E. M. Knox and R. T. Ng, "Algorithms for mining distance-based outliers in large datasets,"International Conference on Very Large Data Bases, 1998, pp 392403.
  14. F. Angiulli and F. Fassetti, "Detecting distance-based outliers in streams of data,"in ACM Conference on Information and Knowledge Management, 2007, pp. 811820.
  15. D. Yang, E. A. Rundensteiner, and M. O. Ward, "Neighbor-based pattern detection for windows over streaming data," in Advances in Database Technology, 2009, pp.529540.
  16. V. Niennattrakul, E. Keogh, and C. A. Ratanamahatana, "Data editing techniquesto allow the application of distance-based outlier detection to streams," in IEEEInternational Conference on Data Mining. IEEE, 2010, pp. 947952.
  17. M. Elahi, K. Li,W. Nisar, X. Lv, and H.Wang, "Efficient clusteringbased outlierdetection algorithm for dynamic data stream," international Conference on FuzzySystems and Knowledge Discovery, vol. 5, 2008, pp. 298304.
  18. M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander, "LOF: identifying density-based local outliers," in ACM SIGMOD, vol. 29, no. 2, 2000, pp. 93104.
  19. S. Papadimitriou, H. Kitagawa, P. B. Gibbons, and C. Faloutsos, "Loci: Fast outlier detection using the local correlation integral," in International Conference on DataEngineering, 2003, pp. 315326.
  20. A.pingale,K. Metre “ A Review of Computing Hotistic Measures on MRCube using MapReduce” in Joumal of lnnovation in Electronics and Comtrunication.
  21. Amol J. Gosavi, Dr. K. V. Metre,” Discovering High Utility Itemsets using Hybrid Approach” in International Journal on Recent and Innovation Trends in Computing and Communication.
  22. Harshali D. Gangurde,K .Metre “Clustering based Feature Selection from High Dimensional data” in International Journal on Recent and Innovation Trends in Computing and Communication

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

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Published

2018-06-30

Issue

Volume 3, Issue 5, May-June-2018

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Ashwini Jadhav, Kalpana Metre, " A Review on Various Outlier Detection Techniques, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.710-715, May-June-2018.