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Ranking with Distance based Outlier Detection Techniques: A Survey
by
Jitendra R. Chandvanya,
Rajanikanth Aluvalu
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 89 - Number 6 |
| Year of Publication: 2014 |
| Authors: Jitendra R. Chandvanya, Rajanikanth Aluvalu |
10.5120/15505-4207
|
Jitendra R. Chandvanya, Rajanikanth Aluvalu . Ranking with Distance based Outlier Detection Techniques: A Survey. International Journal of Computer Applications. 89, 6 ( March 2014), 8-11. DOI=10.5120/15505-4207
@article{
10.5120/15505-4207,
author = {
Jitendra R. Chandvanya,
Rajanikanth Aluvalu
},
title = { Ranking with Distance based Outlier Detection Techniques: A Survey },
journal = {
International Journal of Computer Applications
},
issue_date = { March 2014 },
volume = { 89 },
number = { 6 },
month = { March },
year = { 2014 },
issn = { 0975-8887 },
pages = {
8-11
},
numpages = {9},
url = {
https://ijcaonline.org/archives/volume89/number6/15505-4207/
},
doi = { 10.5120/15505-4207 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:08:30.900951+05:30
%A Jitendra R. Chandvanya
%A Rajanikanth Aluvalu
%T Ranking with Distance based Outlier Detection Techniques: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 89
%N 6
%P 8-11
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract
Outlier Detection is very much popular in Data Mining field and it is an active research area due to its various applications like fraud detection, network sensor, email spam, stock market analysis, and intrusion detection and also in data cleaning. Here we will study some outlier detection technique which are mainly based on distance-based outlier detection with ranking approach and give some idea about the new technique which we will implement in future.
References
- LUKAS A. KURGAN and PETR MUSILEK, Department of Electrical and Computer Engineering, University of Alberta," A survey of Knowledge Discovery and Data Mining process models", The Knowledge Engineering Review, Vol. 21:1, 1–24. ? 2006, Cambridge University
- Wenke Lee, Salvatore J. Stolfo, Kui W. Mok Computer Science Department, Columbia University," A Data Mining Framework for Building Intrusion Detection Models",IEEE-1999
- Ding, W. and Marchionini, G. 1997 A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.
- VARUN CHANDOLA University of Minnesota, "Outlier Detection: A Survey"
- Karanjit Singh and Dr. Shuchita Upadhyaya, Department of Computer Science and Applications, Kurukshetra University Kurukshetra, Haryana, India "Outlier Detection: Applications And Techniques", IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012
- E. Knorr and R. Ng. Algorithms for Mining Distance-Based Outliers in Large Datasets. In Proceedings of VLDB'98, pages 392–403, 1998.
- S. Ramaswamy, R. Rastogi, and K. Shim. Efficient Algorithms for Mining Outliers from Large Data Sets. In Proceedings of SIGMOD'00, pages 427–438, 2000
- Wen Jin1, Anthony K. H. Tung2, Jiawei Han3, and Wei Wang4," Ranking Outliers Using Symmetric Neighborhood Relationship"
- Carlos H. C. Teixeira, Gustavo H. Orair, Wagner Meira Jr, Srinivasan xParthasarathy "An Efficient Algorithm for Outlier Detection in High Dimensional Real Databases"-2008
- Nguyen Hoang VuandVivekanand Gopalkrishnan," Efficient Pruning Schemes for Distance-Based Outlier Detection", W. Buntine et al. (Eds. ): ECML PKDD 2009, Part II, LNAI 5782, pp. 160–175, 2009.
- Rajendra Pamula,Jatin, "Distance Based Fast Outlier Detection Method", India Conference (INDICON), 2010 Annual IEEE.
- Gustavo H. Orair Carlos H. C. Teixeira Wagner Meira Jr. , Ye Wang Srinivasan Parthasarathy "Distance Based Outlier Detection: Consolidation and Renewed Bearing" Proceedings of the VLDB Endowment, Vol. 3, No. 2 Copyright 2010 VLDB Endowment 21508097/10/09. . . $ 10. 00
- Rajendra Pamula, Jatindra Kumar Deka, Sukumar Nandi," An Outlier DetectionMethod based on Clustering", 2011 Second International Conference on Emerging Applications of Information Technology © 2011 IEEE.
- Kanishka Bhaduri, "Algorithms for speeding up distance-based outlier Detection", SIGKDD '11 San Diego, CA, USA
- Ye Wang ,Srinivasan Parthasarathy, Shirish Tatikonda,"Locality Sensitive Outlier Detection: A Ranking Driven Approach" Computer Science and Engineering Department, The Ohio State University, OH, USA,2011
- Ms. S. D. Pachgade, Ms. S. S. Dhande, " Outlier Detection over Data Set Using Cluster-Based and Distance-Based Approach", Volume 2, Issue 6, June 2012 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering.
- Yanyan Huang, Zhongnan Zhang*, Minghong Liao, Yize Tan, Shaobin Zhou," A Hybrid Distance-Based Outlier Detection Approach,2012 International Conference On System and Informatics(ICSAI 2012),987-1-4673-0199-2/12/$31. 00 © 2012 IEEE.
- Vijay Kumar, Sunil Kumar, Ajay Kumar Singh," Outlier Detection: A Clustering-Based Approach", International Journal of Science and Modern Engineering (IJISME), ISSN: 2319-6386, Volume-1, Issue-7, June 2013
- Sakthi Nathiarasan A ,M. E- Student, " Algorithm for Outlier Detection Based on Utility and Clustering (ODUC)", Department of Computer Science and Engineering Adhiyamaan College of Engg , Hosur, India, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013
- M. Wu and C. Jermaine. A bayesian method for guessing the extreme values in a data set? In VLDB '07: Proceedings of the 33rd international conference on Very large data bases, pages 471–482. VLDB Endowment, 2007
- H. Huang, K. Mehrotra, C. K. Mohan," Rank-Based Outlier Detection", Electrical Engineering and Computer Science Technical Reports. Paper 47,2011
- M. M. Breunig, H. -P. Kriegel, R. T. Ng, and J. Sander, Lof: Identifying density-based local outliers," In Proceedings of the ACM SIGMOD International Conference on Management of Data. ACM Press, pp. 93{104, 2000.
Index Terms
Computer Science
Information Sciences
Keywords