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First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007)
An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
Madrid, Spain
September 20-September 21
ISBN: 0-7695-2886-4
ASCII Text
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Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae, "An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method," Empirical Software Engineering and Measurement, International Symposium on, pp. 443-445, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), 2007.
 
 
BibTex
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@article{ 10.1109/ESEM.2007.49,
author = {Kyung-A Yoon and Oh-Sung Kwon and Doo-Hwan Bae},
title = {An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method},
journal ={Empirical Software Engineering and Measurement, International Symposium on},
volume = {0},
year = {2007},
issn = {1938-6451},
pages = {443-445},
doi = {http://doi.ieeecomputersociety.org/10.1109/ESEM.2007.49},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Empirical Software Engineering and Measurement, International Symposium on
TI - An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
SN - 1938-6451
SP443
EP445
A1 - Kyung-A Yoon,
A1 - Oh-Sung Kwon,
A1 - Doo-Hwan Bae,
PY - 2007
KW - null
VL - 0
JA - Empirical Software Engineering and Measurement, International Symposium on
ER -
 
Kyung-A Yoon, Korea Advanced Institute of Science and Technology, Korea
Oh-Sung Kwon, Korea Advanced Institute of Science and Technology, Korea
Doo-Hwan Bae, Korea Advanced Institute of Science and Technology, Korea
The quality of software measurement data affects the accuracy of project manager?s decision making using estimation or prediction models and the understanding of real project status. During the software measurement implementation, the outlier which reduces the data quality is collected, however its detection is not easy. To cope with this problem, we propose an approach to outlier detection of software measurement data using the k-means clustering method in this work.
Citation:
Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae, "An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method," esem, pp.443-445, First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), 2007
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