CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Outliers Detection using Subspace Method: A Survey

by Supriya Garule, Sharmila.m.shinde
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 112 - Number 16
Year of Publication: 2015
Authors: Supriya Garule, Sharmila.m.shinde
10.5120/19751-1580

Supriya Garule, Sharmila.m.shinde . Outliers Detection using Subspace Method: A Survey. International Journal of Computer Applications. 112, 16 ( February 2015), 20-22. DOI=10.5120/19751-1580

@article{ 10.5120/19751-1580,
author = { Supriya Garule, Sharmila.m.shinde },
title = { Outliers Detection using Subspace Method: A Survey },
journal = { International Journal of Computer Applications },
issue_date = { February 2015 },
volume = { 112 },
number = { 16 },
month = { February },
year = { 2015 },
issn = { 0975-8887 },
pages = { 20-22 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume112/number16/19751-1580/ },
doi = { 10.5120/19751-1580 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:49:39.337865+05:30
%A Supriya Garule
%A Sharmila.m.shinde
%T Outliers Detection using Subspace Method: A Survey
%J International Journal of Computer Applications
%@ 0975-8887
%V 112
%N 16
%P 20-22
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Outliers detection is currently very active area of research in data set mining community. Outliers detection is an important research problem that aims to find objects that are considerably dissimilar, exceptional and inconsistent in the database. In this paper, we present a survey of outliers detection techniques using subspace method. The survey will not only cover the high dimensional datasets but also review the more recent developments that deal with more complex outliers detection problems in high-dimensional dataset.

References
  1. E. Muller, I. Assent, U. Steinhausen, and T. Seidl,Outlier Ranking via Subspace Analysis in Multiple Views of the Data, in ICDM, 2012.
  2. R. Agrawal, J. Gehrke, D. Gunopulos, and P. Raghavan, Automatic subspace clustering of high dimensional data for data mining applications, in SIGMOD, 1998,pp. 94105.
  3. C. C. Aggarwal, Outlier Analysis, in Springer,2013, pp. 6172.
  4. M. L. Yiu and N. Mamoulis, Frequent-pattern based iterative projected clusterng, in ICDM, 2003, pp. 689692.
  5. K. Sequeira and M. Zaki, SCHISM: A new approach for interesting subspace mining, in ICDM, 2004, pp. 186193.
  6. Ji Zhang , Advancements of Outlier Detection: A Survey , in ICST Transactions on Scalable Information Systems January-March 2013, Volume 13 ,Issue 01-03.
  7. Assent, R. Krieger, E. Muller, and T. Seidl, INSCY: Indexing subspace clusters with in-process-removal of redundancy,in ICDM, 2008, pp. 719724.
  8. G. Moise and J. Sander, Finding non-redundant, statistically significant regions in high dimensional data: a novel approach to projected and subspace clustering,in KDD, 2008, pp. 533 541.
  9. E. Muller, I. Assent, S. Gunnemann, R. Krieger, and T. Seidl,Relevant Subspace Clustering: Mining the Most Interesting Non-redundant Concepts in High Dimensional Data, in ICDM, 2009, pp. 377386.
  10. K. Sim, V. Gopalkrishnan, A. Zimek, and G. Cong, A Survey on Enhanced Subspace Clustering, DMKD, 2012.
  11. E. Muller, S. Gunnemann, I. Assent, and T. Seidl, Evaluating clustering in subspace projections of high dimensional data,PVLDB, vol. 2, no. 1, pp. 12701281,2009.
  12. C. C. Aggarwal and P. S. Yu, Outlier detection for high dimensional data, in SIGMOD, 2001, pp. 3746.
  13. H. -P. Kriegel, E. Schubert, A. Zimek, and P. Kroger, Outlier detection in axis-parallel subspaces of high dimensional data,in PAKDD, 2009, pp. 831838.
  14. E. Muller, M. Schiffer, and T. Seidl, Statistical selection of relevant subspace projections for outlier ranking, in ICDE,2011, pp. 434445.
  15. E. Muller, I. Assent, U. Steinhausen, and T. Seidl, OutRank:ranking outliers in high dimensional data, in ICDE Workshops,DBRank. IEEE, 2008, pp. 600603.
  16. P. Rousseeuw and A. Leroy, Robust Regression and Outlier Detection. Wiley,1987.
  17. V. Chandola, A. Banerjee, and A. Kumar, Anomaly detection:A survey, ACM Computing Surveys, Vol. 41, No. 3, July 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Outliers Detection High-dimensional Datasets Subspace Outliers Ranking.

Powered by PhDFocusTM