CFP last date
20 May 2024
Call for Paper
June Edition
IJCA solicits high quality original research papers for the upcoming June edition of the journal. The last date of research paper submission is 20 May 2024

Submit your paper
Know more
The week's pick
Responsive image
Enhancing Privacy Preservation: Multi-Attribute Protection with P-Sensitive K-Anonymity

Twinkle Patel Kiran Amin
Random Articles
Reseach Article

Comparative Study of Outlier Detection Algorithms

by Kamaljeet Kaur, Atul Garg
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 147 - Number 9
Year of Publication: 2016
Authors: Kamaljeet Kaur, Atul Garg
10.5120/ijca2016911176

Kamaljeet Kaur, Atul Garg . Comparative Study of Outlier Detection Algorithms. International Journal of Computer Applications. 147, 9 ( Aug 2016), 21-26. DOI=10.5120/ijca2016911176

@article{ 10.5120/ijca2016911176,
author = { Kamaljeet Kaur, Atul Garg },
title = { Comparative Study of Outlier Detection Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { Aug 2016 },
volume = { 147 },
number = { 9 },
month = { Aug },
year = { 2016 },
issn = { 0975-8887 },
pages = { 21-26 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume147/number9/25682-2016911176/ },
doi = { 10.5120/ijca2016911176 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:51:28.125287+05:30
%A Kamaljeet Kaur
%A Atul Garg
%T Comparative Study of Outlier Detection Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 147
%N 9
%P 21-26
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

As the dimension of the data is increasing day by day, outlier detection is emerging as one of the active area of research. Finding of the outliers from large data sets is the main problem. Outlier is considered as the pattern that is different from the rest of the patterns present in the data set. The detection of the outlier in the data set is an important process as it helps in acquiring the useful information that further helps in the data analysis. Various algorithms have been proposed till date for the detection of the outliers. This paper covers a study of various outlier detection algorithms like Statistical based outlier detection, Depth based outlier detection, Clustering based technique, Density based outlier detection etc. Comparison study of these outlier detection methods is done to find out which of the outlier detection algorithms are more applicable on high dimensional data.

References
  1. A Christy, G. Meera Gandhi, “Cluster Based Outlier Detection Algorithm for Healthcare Data”, Elsevier, Volume 50, 2015, pp 209-215.
  2. A. Rajaraman, J.D. Ullman, “Mining of massive datasets”, Cambridge UniversityPress, Cambridge, 2012.
  3. A. Struyf, P J Rousseeuw, “High-dimensional computation of the deepest location. Computational Statistics and Data Analysis”, American mathematical soc, Volume 34, 2000, pp 415–426.
  4. Anjali Barmad, Madhu M.Nashipudinath, “An Efficient Strategy to Detect Outlier Transactions”, International Journal of Soft Computing and Engineering, Volume 3, Issue 6, January 2014, pp 174-178.
  5. B. Wang, Gang Xiao, Hao Yu,  Xiaochun Yang, “Distance-Based Outlier Detection on Uncertain Data”, IEEE International conference on Computer and Information Technology, Volume 1,October 2009, pp 293 – 298.
  6. Bo Liu, Yanshan Xiao, P.S. Yu, Zhifeng Hao, Longbing Cao, “An Efficient Approach for Outlier Detection with Imperfect Data Labels”, IEEE Transactions on Knowledge and Data Engg, 2014, pp 1602 – 1616.
  7. Charu C. Aggarwal, Philip S, “Outlier Detection for High Dimensional Data”, In proceedings of ACM SIGMOD International conference on Management of data, Volume 30, Issue 2, June 2001, pp 37-46.
  8. D. Xiang, W. Lee, “Information-theoretic measures for anomaly detection”, In proceedings of IEEE Symposium on Security and Privacy, May 2001,pp 130-143.
  9. Dr. T. Christopher, T Divya, “A Study of Clustering Based Algorithm for Outlier Detection in Data streams”, Proceedings of the UGC Sponsored National Conference on Advanced Networking and Applications, March 2015, pp 194-197
  10. Dragoljub Pokrajac, Aleksandar Lazarevic, Longin Jan Latecki, “IEEE Symposium on Computational Intelligence and Data Mining (CIDM)”, April 2007.
  11. F. Anguiulli, F. Fassetti, “Detecting Distance-Based Outliers in Streams of Data”, In Proceedings of the 16th ACM Conference on information and knowledge management (CIKM), 2007, pp 811 – 820.
  12. Fabrizio Angiulli, Stefano Basta, Stefano Lodi, and Claudio Sartori, “Distributed Strategies for Mining Outliers in Large Data Sets”, IEEE Transactions On Knowledge And Data Engineering, Volume 25, No.7, July 2013, pp 1520-1532.
  13. H.S.Behera, “A New Hybridized K-Means Clustering Based Outlier Detection Technique For Effective Data Mining”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 4, April 2012, pp 287-292.
  14. HaNguyen Thi Thu,Quynh Nguyen Huu, Tu Nguyen Thi Hgoc, “A Supervised Learning Method Combine With Dimensionality Reduction in Vietnamese Text Summarization”, IEEE conference on Computing, Communications and IT Applications (ComComAp), April 2013, pp 69-73.
  15. Helmer G, Wong J, Honavar V, Miller L, “Intelligent agents for intrusion detection”, In Proceedings of IEEE Information Technology Conference, July 2013, pp 121-124.
  16. Jayanta K. Dutta, Bonny Banerjee, Chandan K. Reddy, “RODS: Rarity based Outlier Detection in a Sparse Coding Framework”, IEEE Transactions on Knowledge and Data Engineering, Volume 28, Issue 2, September 2015,pp 483-495.
  17. Ji Zhang, “Advancements of Outlier Detection: A Survey”, ICST Transactions on Scalable Information Systems, Volume 13, Issue 01-03, March 2013, pp 1-16.
  18. Jiadong Ren,“Efficient Outlier Detection Algorithm for Heterogeneous Data Streams”, Sixth International Conference on Fuzzy Systems and Knowledge Discovery, 2009.
  19. K. Bhaduri, “Algorithms for speeding up distance-based outlier detection”, In Proceedings of ACM SIGKDD International Conference KDD, New York, USA, 2011, pp 859–867.
  20. Karanjit Singh, “Outlier Detection: Applications and Techniques”, IJCSI International Journal of Computer Science Issues, Volume 9, Issue 1, No 3, January 2012, Pp 307-323.
  21. Liangwei Zhangn, Jing Lin, Ramin Karim, “An anglebased subspace anomaly detection approach to highdimensional data: With an application to industrial fault detection” , Elsevier, 2015, Pp 482-497
  22. Mahito Sugiyama, “Rapid Distance-Based Outlier Detection via Sampling”, Advances in Neural Information Processing Systems 26 (NIPS 2013), 2013.
  23. Manish Gupta, Jing Gao “Outlier Detection for Temporal Data: A Survey”, IEEE transactions on knowledge and data engineering, Vol. 25, No. 1, January 2014, pp 1-20
  24. ManzoorElahi, “DB-Outlier Detection Algorithm using Divide and Conquer approach over Dynamic”, International Conference on Computer Science and Software Engineering DataStream”, 2008.
  25. MaysoonAbulkhair, “Intelligent Integration of Discharge Summary: a Formative Model”, 4th International Conference on Intelligent Systems, Modelling and Simulation, 2013.
  26. Miguel Cardenas Montes, “Depth based outlier detection algorithm”, Springer, 2014, pp 122-132.
  27. Ms. S. D. Pachgade, “Outlier Detection over Data Set Using Cluster-Based and Distance-Based Approach”, International Journal of Advanced Research in Computer Science and Software Engineering ,Volume 2, Issue 6, June 2012, pp 12-16.
  28. P.Chandore, P.Chatur, “Outlier Detection Techniques over Streaming Data in Data Mining: A Research Perspective”, International Journal of Recent Technology and Engineering (IJRTE) ISSN: 2277-3878, Volume-2, Issue-1, March 2013.
  29. Parneeta Dhaliwal, “A Cluster-based Approach for Outlier Detection in Dynamic Data Streams (KORM: k-median OutlieR Miner)”, Journal of computing, volume 2, Issue 2, february 2010, pp 74-80.
  30. Prakash Chandore, “Outlier Detection Techniques over Streaming Data in Data Mining: A Research Perspective”, International Journal of Recent Technology and Engineering, Volume-2, Issue-1, March 2013, pp 157- 162.
  31. R Aggarwal, R Srikant, “Mining sequential patterns”, In Proceedings of 11th International Conference on Data Engineering IEEE Computer Society, Washington, DC, USA, 1995, pp 3-14.
  32. R.Lakshmi Devi, Dr. R.Amalraj “An Efficient Unsupervised Cluster based Hubness Technique for Outlier Detection in High dimensional data”, International Journal of Innovative Research in Advanced Engineering , Volume 2 , Issue 10 , October 2015, pp 63-70.
  33. Rajendra Pamula, Jatindra Kumar Deka, Sukumar Nandi, “An Outlier Detection Method based on Clustering”, Second International Conference on Emerging Applications of information Technology, 2011, pp 253-256.
  34. S S Sreevidya, “A Survey on Outlier Detection Methods”, International Journal of Computer Science and Information Technologies, Volume 5 (6), 2014, pp 8153-8156.
  35. Shuwu, “Information-Theoretic Outlier Detection for Large-Scale Categorical Data”, IEEE transactions on knowledge and data engineering, Volume 25, No. 3, March 2013.
  36. Vijay Kumar, “Outlier Detection: A Clustering-Based Approach”, International Journal of Science and Modern Engineering (IJISME), Volume-1, Issue-7, June 2013, pp 16-19.
  37. W. Fan, M. Miller, S. Stolfo, W. Lee, P. Chan “Using artificial anomalies to detect unknown and known network intrusions”, In Proceedings of IEEE International Conference on Data Mining, IEEE Computer Society, Volume 6, Issue 5, April 2004, pp 507-527.
  38. Y-Shi, “COID: A cluster- outlier iterative detection approach to multi-dimensional data analysis”, Knowledge Information System Volume 288, No 3, 2011, pp 709 – 733.
Index Terms

Computer Science
Information Sciences

Keywords

Outlier Detection Statistical Outlier Detection Density based Clustering Classification.

Powered by PhDFocusTM