Metamorphic exploration of an unsupervised clustering program

Machine learning has been becoming increasingly popular and widely-used in various industry domains. The presence of the oracle problem, however, makes it difficult to ensure the quality of this kind of software. Furthermore, the popularity of machine learning and its application has attracted many users who are not experts in this field. In this paper, we report on using a recently introduced method called metamorphic exploration where we proposed a set of hypothesized metamorphic relations for an unsupervised clustering program, Weka, to enhance understanding of the system and its better use.