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An Effective Genetic Algorithm for Outlier Detection

by P.Vishnu Raja, Dr.V.Murali Bhaskaran
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 38 - Number 6
Year of Publication: 2012
Authors: P.Vishnu Raja, Dr.V.Murali Bhaskaran
10.5120/4694-6836

P.Vishnu Raja, Dr.V.Murali Bhaskaran . An Effective Genetic Algorithm for Outlier Detection. International Journal of Computer Applications. 38, 6 ( January 2012), 30-33. DOI=10.5120/4694-6836

@article{ 10.5120/4694-6836,
author = { P.Vishnu Raja, Dr.V.Murali Bhaskaran },
title = { An Effective Genetic Algorithm for Outlier Detection },
journal = { International Journal of Computer Applications },
issue_date = { January 2012 },
volume = { 38 },
number = { 6 },
month = { January },
year = { 2012 },
issn = { 0975-8887 },
pages = { 30-33 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume38/number6/4694-6836/ },
doi = { 10.5120/4694-6836 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:24:52.658418+05:30
%A P.Vishnu Raja
%A Dr.V.Murali Bhaskaran
%T An Effective Genetic Algorithm for Outlier Detection
%J International Journal of Computer Applications
%@ 0975-8887
%V 38
%N 6
%P 30-33
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

The main objective the outlier detection is to find the data that are exceptional from other data in the data set. Detection of such exceptional data’s is an important issue in many fields like fraud detection, Intrusion detection and Medicine . In this paper we are proposing an algorithm to detect outliers using genetic algorithm. The proposed method was exceptionally accurate in identifying the outliers the datasets that we have tested. The result analysis is done on some standard dataset to view accuracy of the algorithm.

References
  1. Abe,n., Zadrozny., Langford,j: 2006 Outlier Detection by active Learning. SIGKKD-USA
  2. Charu C. Aggarwal and Philip S. Yu. 2001 Outlier detection for high Dimensional data.
  3. E.M.Knorr and R.T.Ng. 1998. Algorithms for mining Distance-Based Outliers in Large DataSets. In Proc- VLDB,pp.392-403
  4. M.M. Breunig , H.P.Kriegel, R.R.Ng, and J.Sander.2000 LOF : Identifying Density – Based Local Outliers. In Proc. SIGMOD conf.pp 93-104
  5. Angiulli, F.Basta, S., Pizzuti 2006. Distance- Based Detection and Prediction of Outliers. IEEE Transactions on Knowledge and Data Engineering.
  6. Provost, F., Fawcett, 2001. Robust Classification of Imprecise environments Machine Learning 42,203-231.
  7. Z.Michalewicz. 1996. Genetic algorithm and Data Structures- Evolution Programs. NY: Springer-verlag.
  8. S.Forrest. “ Genetic Algorithms”. 1996, ACM Computer society,sum., vol.28.,pp 77 – 80.
  9. W, Banzhar, P.Nordin, R.Keller, and E Francone 1998- Genetic Programming on the automatic evolution of computer program and its applications. Morgan Kaufmmn Publishers.
  10. David E. Goldberg -2005- Genetic algorithm in Search, Optimization and Machine Learning.
  11. C.Aggarwal and P.Yu -2001- Outlier detection for high dimensional data” In Proceedings of the ACM SIGMOD International Conference of management of data , volume 30, issue 2, pages 37-46.
  12. M.Breunig, H.P.Kriegel R.Ng and J.Sander, 2000 “ LOF: Identifying Density Based Local Outliers”. In Proceedings of the 2000 ACM SIGMOD International Conference of Management of Data Pages 93-104.
  13. M.Brito, E.Chavez, A.Quiroz and J.Yukich-1997- “ Connectivity of the mutual K-Nearest Neighbor Graph in Clustering and Outlier Detection”. Statistics and Probability Letters, volume 35, Issue 1, Pages 33-42.
  14. Z.He, X.Xu and S.Deng, June 2003 “ Discovery Cluster based Local Outliers “ Pattern Recognition Letters, Volume 24, Issue 9-10, Pages 1641-1650
  15. V.Hautamaki, I.Karkkainen and P.Franti, -2004- “ Outlier Detection Using K-Nearest Neighbor Graph”. In Proceedings of the international Conference on Pattern Recognition”. Volume 3, Pages 430 – 433.
  16. M.Jaing, S.Tseng and C.Su, -2001- “ Two Phase Clustering Process for Outlier Detection”.
  17. Knorr, E.M., Ng,R.T.-1997- A Unified Notion of Outliers : Properties and Computation . In Proceedings of the 3rd International Conference on Knowledge Discovery and Data Mining Proceedings .,PP 219-222.
  18. UCI Repository www.ics.uci.edu/mlearn/ MLRepositary .html
  19. V.Bamett and T.Lewis. Outliers in Statistical Data.
Index Terms

Computer Science
Information Sciences

Keywords

Outliers Genetic algorithm Anomalies Exceptional objects Optimization.

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