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

Authors:
Kyung-A Yoon;Oh-Sung Kwon;Doo-Hwan Bae
Affiliations:
Korea Advanced Institute of Science and Technology, Korea;Korea Advanced Institute of Science and Technology, Korea;Korea Advanced Institute of Science and Technology, Korea
Venue:
ESEM '07 Proceedings of the First International Symposium on Empirical Software Engineering and Measurement
Year:
2007

Citing 0
Cited 6

Mining Top-n Local Outliers in Constrained Spatial Networks

ADMA '08 Proceedings of the 4th international conference on Advanced Data Mining and Applications
A pattern-based outlier detection method identifying abnormal attributes in software project data

Information and Software Technology
Fuzzy clustering-based approach for outlier detection

ACE'10 Proceedings of the 9th WSEAS international conference on Applications of computer engineering
New outlier detection method based on fuzzy clustering

WSEAS Transactions on Information Science and Applications
Quality indicators for business process models from a gateway complexity perspective

Information and Software Technology
Software mining and fault prediction

Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery

Quantified Score

Hi-index	0.00

Visualization

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.