An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method

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http://doi.ieeecomputersociety.org/10.1109/ESEM.2007.49

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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	x
	@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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