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Outlier Detection for Business Intelligence using Data Mining Techniques

by Mohiuddin Ali Khan, Sateesh Kumar Pradhan, M. A. Khaleel
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 106 - Number 2
Year of Publication: 2014
Authors: Mohiuddin Ali Khan, Sateesh Kumar Pradhan, M. A. Khaleel
10.5120/18493-9555

Mohiuddin Ali Khan, Sateesh Kumar Pradhan, M. A. Khaleel . Outlier Detection for Business Intelligence using Data Mining Techniques. International Journal of Computer Applications. 106, 2 ( November 2014), 28-31. DOI=10.5120/18493-9555

@article{ 10.5120/18493-9555,
author = { Mohiuddin Ali Khan, Sateesh Kumar Pradhan, M. A. Khaleel },
title = { Outlier Detection for Business Intelligence using Data Mining Techniques },
journal = { International Journal of Computer Applications },
issue_date = { November 2014 },
volume = { 106 },
number = { 2 },
month = { November },
year = { 2014 },
issn = { 0975-8887 },
pages = { 28-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume106/number2/18493-9555/ },
doi = { 10.5120/18493-9555 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:38:20.067831+05:30
%A Mohiuddin Ali Khan
%A Sateesh Kumar Pradhan
%A M. A. Khaleel
%T Outlier Detection for Business Intelligence using Data Mining Techniques
%J International Journal of Computer Applications
%@ 0975-8887
%V 106
%N 2
%P 28-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we have made a review of various outlier detection techniques from data mining perspective. Existing studies in data mining focus generally on finding patterns from large datasets and using it for organizational decision making. However, finding exceptions and outliers did not receive much attention in the data mining field as other topics received. Finally, this paper concludes some advances in outlier detection recently.

References
  1. Data Mining:Concepts and Techniques, Third Edition, Jiawei Han and Micheline Kamber, ISBN-13, 978-0123814791.
  2. Data mining techniques by Arun K. Pujari, ISBN 9788173713804.
  3. Application of k-Means Clustering algorithm for prediction of Students' Academic Performance, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, no. 1, 2010.
  4. Data Mining Techniques for E-Business Intelligence, International Journal of Scientific & Engineering Research, Volume 4, Issue 10, October 2013, ISSN 2229-5518 by Mohiuddin Ali Khan and Sateesh K Pradhan.
  5. Mining students behavior in web-based learning programs Man Wai Lee, Sherry Y. Chen, Kyriacos Chrysostomou, Xiaohui Liu Expert Syst. Appl. 36(2): 3459-3464 (2009).
  6. Application of k-Means Clustering algorithm for prediction of Students' Academic Performance, (IJCSIS) International Journal of Computer Science and Information Security, Vol. 7, no. 1, 2010.
  7. Data mining in course management systems: Moodle case study and tutorialCristo´bal Romero , Sebastia´n Ventura, Enrique Garc?´a, Volume 51 Issue 1, August, 2008.
  8. A. Merceron and K. Yacef, " Educational Data Mining: A Case study," in 'Proc. Int. Conf. Artif Intell Educ. , Amsterdam, The Netherlands, 2005 pp 1-8.
  9. A. F. D. Costa and J. T Lopes. Os Estudantes e os seus Trajectos no Ensino Superior: Sucesso e Insucesso, Factores e processos, Promocao de Boas Praticas. 2008 Retrieved July 2009 from http:// etes. cies. iscte. pt
  10. Aggarwal, C. C. , Yu, S. P. , "An effective and efficient algorithm for high-dimensional outlier detection, The VLDB Journal, 2005, vol. 14, pp. 211-221.
  11. Yufeng Kou, Chang-Tien Lu, Dos Santos, R. F. " Spatial Outlier Detection: A Graph-Based Approach", ICTAI 2007,Volume 1, 2007,pp. 281 – 288.
  12. Y. Kou, C. -T. Lu, and D. Chen. "Spatial weighted outlier detection". In Proceedings of the Sixth SIAM International Conference on Data Mining, pp. 614–618, Bethesda,Maryland, USA, 2006.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Outlier Business Intelligence Architecture.

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