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11

ESEM
2007
ACM

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

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

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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.

Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae

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ESEM 2007 | Outlier Detection | Software Engineering | Software Measurement Data | Software Measurement Implementation |

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Added	19 Oct 2010
Updated	19 Oct 2010
Type	Conference
Year	2007
Where	ESEM
Authors	Kyung-A Yoon, Oh-Sung Kwon, Doo-Hwan Bae

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