CFP last date
22 April 2024
Reseach Article

Survey on Outlier Detection in Data Mining

by Janpreet Singh, Shruti Aggarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 67 - Number 19
Year of Publication: 2013
Authors: Janpreet Singh, Shruti Aggarwal
10.5120/11506-7223

Janpreet Singh, Shruti Aggarwal . Survey on Outlier Detection in Data Mining. International Journal of Computer Applications. 67, 19 ( April 2013), 29-32. DOI=10.5120/11506-7223

@article{ 10.5120/11506-7223,
author = { Janpreet Singh, Shruti Aggarwal },
title = { Survey on Outlier Detection in Data Mining },
journal = { International Journal of Computer Applications },
issue_date = { April 2013 },
volume = { 67 },
number = { 19 },
month = { April },
year = { 2013 },
issn = { 0975-8887 },
pages = { 29-32 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume67/number19/11506-7223/ },
doi = { 10.5120/11506-7223 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:25:54.411704+05:30
%A Janpreet Singh
%A Shruti Aggarwal
%T Survey on Outlier Detection in Data Mining
%J International Journal of Computer Applications
%@ 0975-8887
%V 67
%N 19
%P 29-32
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data Mining is used to extract useful information from a collection of databases or data warehouses. In recent years, Data Mining has become an important field. This paper has surveyed upon data mining and its various techniques that are used to extract useful information such as clustering, and has also surveyed the techniques that are used to detect the outliers. This paper also presents various techniques used by different researchers to detect outliers and present the efficient result to the user.

References
  1. Venkatadri. M, Dr. Lokanatha C. Reddy"A Review on Data mining from Past to the Future" International Journal of Computer Applications (0975 –8887) Volume 15– No. 7, February 2011.
  2. Heikki, Mannila. 1996" Data mining: machine learning, statistics, and databases" IEEE.
  3. Han, J. and M. Kamber, 2006. Data Mining: Concepts and Techniques, Morgan Kaufmann, 2nd Ed.
  4. Dhanashree S. Deshpande "A survey on web Data Mining Application" Emerging Trends in Computer Science and Information Technology -2012.
  5. H. S Behera, Rosly Boy,Lingdoh, Diptendra Kodama singh "An Improved hybridized k-means clustering algorithm for high dimensional data set & it's performance analysis" International Journal on Computer Science and Engineering (IJCSE).
  6. M. Vijayalakshmi, M. Renuka Devi "A Survey of different issue of different clustering algorithms used in large data sets" International Journal of Advanced Research in Computer Science and Software Engineering, Volume 2, Issue 3,March2012.
  7. M. Livny, R. Ramakrishnan, T. Zhang, 1996. " BIRCH: An Efficient Clustering Method for Very Large Databases". Proceeding ACMSIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery.
  8. S. Guha, R. Rastogi, and K. Shim, 1998. "CURE: An Efficient Clustering Algorithm for Large Databases". Proc. ACM Int'l Conf. Management of Data.
  9. H. S. Behera, AbhishekGhosh, SipakkuMishra "A New Hybridized K-Means Clustering Based Outlier Detection Technique for Effective Data Mining "International Journal of Advanced Research in Computer Science and Software Engineeing, Volume 2, Issue 4, April 2012.
  10. Markus M. Breunig, Hans-Peter Kriegel, Raymond T. Ng, Jörg Sander" LOF: Identifying Density-Based Local Outliers".
  11. Raghuvira Pratap, K Suvarna, J Rama Devi, Dr. K Nageswara Rao "Efficient Density based Improved K- Medoids " International Journal of Advanced Computer Science and Applications, Vol. 2, No. 6, 2011.
  12. RajashreeDash, Debahuti Mishra, Amiya Kumar Rath, MiluAcharya " A hybridized K-means clustering approach for high dimensional dataset" International Journal of Engineering, Science and Technology, Vol. 2, No. 2, 2010.
  13. Moh?d Belal Al-Zoubi "An Effective Clustering-Based Approach for Outlier Detection" European Journal of Scientific Research Vol. 28 No. 2 (2009).
  14. Parneeta Dhaliwal, MPS Bhatia and Priti Bansal "A Cluster-based Approach for Outlier Detection in Dynamic Data Streams" Vol. 2, Issue 2, Feb 2010.
  15. Ms. S. D. Pachgade, Ms. S. S. Dhande "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 4, June 2012.
  16. S. Vijayarani S. Nithya" An Efficient Clustering Algorithm for Outlier Detection" International Journal of Computer Applications (0975 – 8887) Volume 32– No. 7, October 2011.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Clustering Outlier Outlier Detection