Title :
An Approach to Outlier Detection of Software Measurement Data using the K-means Clustering Method
Author :
Yoon, Kyung-A ; Kwon, Oh-Sung ; Bae, Doo-Hwan
Author_Institution :
Korea Adv. Inst. of Sci. & Technol., Daejeon
Abstract :
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.
Keywords :
pattern clustering; software metrics; software quality; k-means clustering method; outlier detection; software measurement data quality; Clustering algorithms; Clustering methods; Data mining; Decision making; Euclidean distance; Predictive models; Project management; Quality management; Software engineering; Software measurement;
Conference_Titel :
Empirical Software Engineering and Measurement, 2007. ESEM 2007. First International Symposium on
Conference_Location :
Madrid
Print_ISBN :
978-0-7695-2886-1
DOI :
10.1109/ESEM.2007.49
