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ESEM
2007
ACM

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

12 years 3 months ago
An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
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
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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