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Reseach Article

Detection of Discordant Observations and Visualization of Data

Published on None 2011 by Loshma Gunisetti
International Conference and Workshop on Emerging Trends in Technology
Foundation of Computer Science USA
ICWET - Number 9
None 2011
Authors: Loshma Gunisetti
db33eadb-f39f-4802-a110-f7d68d68e90b

Loshma Gunisetti . Detection of Discordant Observations and Visualization of Data. International Conference and Workshop on Emerging Trends in Technology. ICWET, 9 (None 2011), 15-18.

@article{
author = { Loshma Gunisetti },
title = { Detection of Discordant Observations and Visualization of Data },
journal = { International Conference and Workshop on Emerging Trends in Technology },
issue_date = { None 2011 },
volume = { ICWET },
number = { 9 },
month = { None },
year = { 2011 },
issn = 0975-8887,
pages = { 15-18 },
numpages = 4,
url = { /proceedings/icwet/number9/2134-db302/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference and Workshop on Emerging Trends in Technology
%A Loshma Gunisetti
%T Detection of Discordant Observations and Visualization of Data
%J International Conference and Workshop on Emerging Trends in Technology
%@ 0975-8887
%V ICWET
%N 9
%P 15-18
%D 2011
%I International Journal of Computer Applications
Abstract

Discordant Observations are special values or extraordinary cases in the available data which deviate so much from other observations so as to arouse suspicions that they were generated by a different mechanism. They can be used to identify special or extraordinary or fraudulent cases in day to day transactions. Preprocessing can be used to identify the noise in the data and removal of such noise improves data quality. Discordant Observations are also called Anomalies or Outliers. Anomaly Detection can be used for Traffic Analysis, Credit Card Fraud Detection. We applied Anomaly Detection to Traffic data set for identifying the anomaly traffic stations on the highway. Detected stations represent abnormalities in the traffic sensors data. This information is used by us to identify the faulty traffic sensors located at the highway stations. Two dimensional visualization of the outliers has been provided which can be used for analyzing the data in an efficient manner. Traffic Management becomes easier when the abnormal traffic sensors identified at the corresponding stations are identified. The method used here can be easily applied to very large datasets.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Discordant Observations Anomaly Outlier