CFP last date
20 March 2024
Reseach Article

Survey on Outlier Detection in Data Stream

by Pooja Thakkar, Jay Vala, Vishal Prajapati
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 136 - Number 2
Year of Publication: 2016
Authors: Pooja Thakkar, Jay Vala, Vishal Prajapati
10.5120/ijca2016908257

Pooja Thakkar, Jay Vala, Vishal Prajapati . Survey on Outlier Detection in Data Stream. International Journal of Computer Applications. 136, 2 ( February 2016), 13-16. DOI=10.5120/ijca2016908257

@article{ 10.5120/ijca2016908257,
author = { Pooja Thakkar, Jay Vala, Vishal Prajapati },
title = { Survey on Outlier Detection in Data Stream },
journal = { International Journal of Computer Applications },
issue_date = { February 2016 },
volume = { 136 },
number = { 2 },
month = { February },
year = { 2016 },
issn = { 0975-8887 },
pages = { 13-16 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume136/number2/24124-2016908257/ },
doi = { 10.5120/ijca2016908257 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T23:35:56.515413+05:30
%A Pooja Thakkar
%A Jay Vala
%A Vishal Prajapati
%T Survey on Outlier Detection in Data Stream
%J International Journal of Computer Applications
%@ 0975-8887
%V 136
%N 2
%P 13-16
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining provides a way for finding hidden and useful knowledge from the large amount of data .usually we find any information by finding normal trends or distribution of data .But sometimes rare event or data object may provide information which is very interesting to us .Outlier detection is one of the task of data mining .It finds abnormal data point or sequence hidden in the dataset .Data stream is unbounded sequence of data with explicit or implicit temporal context .Data stream is uncertain and dynamic in nature. Traditional outlier detection techniques for static data which require whole dataset for modelling is not suitable for data stream because whole data stream cannot be stored. Network intrusion detection ,web click stream analysis ,fraud detection ,fault detection in machines ,sensor data analysis are some of the applications of data stream outlier detection .In this paper, we have described several issues in data stream outlier detection and usual approaches or techniques for finding outlier in data stream .

References
  1. Jiawei Han, Micheline Kamber and Jian Pei, “Data mining Concepts and Techniques”, Third Edition, Morgan Kaufmann Series in Data management Systems.
  2. Charu C. Aggarwal, “Outlier Analysis”, Springer, 2013.
  3. Charu C. Aggarwal, “Data Mining: The Textbook”, Springer, 2015.
  4. QIANG YANG, “10 CHALLENGING PROBLEMS IN DATA MINING RESEARCH”,International Journal of Information Technology & Decision Making Vol. 5, No. 4 ,2006.
  5. Karanjit Singh and Dr. Shuchita Upadhyaya, “Outlier Detection: Applications And Techniques”, IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 1, No 3, January 2012.
  6. Sreevidya S S, “A Survey on Outlier Detection Methods”, (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (6), 2014.
  7. Ji Zhang, “Advancements of Outlier Detection :A Survey”, ICST Transactions on Scalable Information Systems ,January-March 2013 , Volume 13 issue 01-03 ,e2.
  8. Manish Gupta, Jing Gao, Member, IEEE, Charu C. Aggarwal, Fellow, IEEE, and Jiawei Han, Fellow, IEEE , “Outlier Detection for Temporal Data: A Survey”, IEEE
  9. Shiblee Sadik and Le Gruenwald, “Research Issues in Outlier Detection for Data Streams”, SIGKDD Explorations, Volume 15, Issue 1.
  10. Neeraj Chugh, Mitali Chugh, Alok Agarwal, “Outlier Detection in Streaming Data A research Perspective”, International Conference on Parallel, Distributed and Grid Computing, IEEE, 2014.
  11. Fabrizio Angiulli, Fabio Fassetti, “Detecting Distance-Based Outliers in Streams of Data”, ACM, 2007.
  12. Md. Shiblee Sadik, Le Gruenwald, “DBOD-DS: Distance Based Outlier Detection for Data Streams”, dexa, 2010.
  13. Dragoljub Pokrajac, Aleksandar Lazarevic, Longin Jan Latecki, “ Incremental Local Outlier Detection for Data Streams”, IEEE Symposium on Computational Intelligence and Data Mining (CIDM), April 2007.
  14. Seyed Hesamodin Karimian, Manouchehr Kelarestaghi, Sattar Hashemi, “I-IncLOF: Improved Incremental Local Outlier Detection for Data Streams”, The 16th CSI International Symposium on Artificial Intelligence and Signal Processing, IEEE, 2012.
  15. Xiaoke SU, Yang LAN, “Sliding Window-based Outlier Detection in Mixed Data Stream”, Journal of Computational Information Systems 6:14, 2010.
  16. Yu Xiang, Lei Guohua1, Xu Xiandong Lin Liandong, “A Data Stream Outlier Detection Algorithm Based on Grid”, IEEE, 2015.
  17. Amineh Amini and Teh Ying Wah, “Requirements for Clustering Evolving Data Stream”, 2nd International Conference on Soft Computing and its Applications (ICSCA'2013) Sept. 25-26, 2013.
  18. Yogita Thakran, Durga Toshniwal, “Unsupervised Outlier Detection in Streaming Data Using Weighted Clustering”, IEEE, 2012.
  19. Varun Chandola, Arindam Banerjee, Vipin Kumar, Aomaly Detection: A Survey”, ACM Computing Surveys, 2009.
Index Terms

Computer Science
Information Sciences

Keywords

Data mining Outliers data stream mining.