Data mining: concepts and techniques
Data mining: concepts and techniques
Data Mining: Concepts, Models, Methods and Algorithms
Data Mining: Concepts, Models, Methods and Algorithms
Software Risk Management: Principles and Practices
IEEE Software
Empirical Software Engineering
Empirical Software Engineering
Defect Data Analysis Based on Extended Association Rule Mining
MSR '07 Proceedings of the Fourth International Workshop on Mining Software Repositories
ICSP'08 Proceedings of the Software process, 2008 international conference on Making globally distributed software development a success story
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In this paper, characteristics of a runaway project are revealed based on combinations of risk factors which appear in the project. Concretely, an association rule mining technique is applied with an actual questionnaire data to induce rules that associate combinations of risk factors with runaway status of software projects. Furthermore, the induced rules are integrated and reduced based on a certain rule obtained from experts' perception to simplify the representation of characteristics of a runaway project. Then, for confirming the effectiveness of this characterization, it is evaluated how many runaway projects in distinct data set were identified by the reduced rules. The result of the experiment suggested that the induced rules are effective to characterize runaway projects.