A Review on Various Outlier Detection Techniques

Authors(2) :-Ashwini Jadhav, Kalpana Metre

Outlier detection or anomaly detection is important branch data mining. It monitors the data and extracts the unusual events from dataset. The outlier detection technique can be applied in variety of domains such as detection of intrusion , fraud analysis, human gait analysis etc. The outlier detection strategies vary with respect to the application requirement. The outlier can be extracted from static dataset as well as from continuously streaming data. This work aims to study various outlier detection strategies and its limitation. After analysis of existing techniques, a streaming based local outlier detection technique is proposed.

Authors and Affiliations

Ashwini Jadhav
Computer Department Savitribai Phule Pune University, Nashik, Maharashtra, India
Kalpana Metre
Computer Department, Savitribai Phule Pune University, Nashik, Maharashtra, India

Outlier Detection, Anomaly Detection, Distance Based Outlier, Local Outlier, Global Outlier, Memory Efficiency

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Publication Details

Published in : Volume 3 | Issue 5 | May-June 2018
Date of Publication : 2018-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 710-715
Manuscript Number : CSEIT1835136
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Ashwini Jadhav, Kalpana Metre, "A Review on Various Outlier Detection Techniques", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 3, Issue 5, pp.710-715, May-June-2018.
Journal URL : http://ijsrcseit.com/CSEIT1835136

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