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      A Validation and Quality Assessment Method with Metamorphic Relations for Unsupervised Machine Learning Software

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          Abstract

          Unsupervised machine learning is a task of modeling the underlying structure of ``unlabeled data''. Since learning algorithms have been incorporated into many real-world applications, validating the implementations of those algorithms becomes much more important in the aim of software quality assurance. However, validating unsupervised machine learning programs is challenging because there lacks of priori knowledge. Along this line, in this paper, we present a metamorphic testing based method for validating and characterizing unsupervised machine learning programs, and conduct an empirical study on a real-world machine learning tool. The results demonstrate to what extent a program may fit to the profile of a specific scenario, which help end-users or software practitioners comprehend its performance in a vivid and light-weight way. And the experimental findings also reveal the gap between theory and implementation in a software artifact which could be easily ignored by people without much practical experience. In our method, metamorphic relations can serve as one type of quality measure, and one type of guidelines for selecting suitable programs.

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          Most cited references 10

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          Data analysis plays an indispensable role for understanding various phenomena. Cluster analysis, primitive exploration with little or no prior knowledge, consists of research developed across a wide variety of communities. The diversity, on one hand, equips us with many tools. On the other hand, the profusion of options causes confusion. We survey clustering algorithms for data sets appearing in statistics, computer science, and machine learning, and illustrate their applications in some benchmark data sets, the traveling salesman problem, and bioinformatics, a new field attracting intensive efforts. Several tightly related topics, proximity measure, and cluster validation, are also discussed.
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              An empirical comparison of supervised learning algorithms

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                Author and article information

                Journal
                27 July 2018
                Article
                1807.10453

                http://arxiv.org/licenses/nonexclusive-distrib/1.0/

                Custom metadata
                Zhiyi Zhang and Xiaoyuan Xie are the co-first authors
                cs.SE

                Software engineering

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