Metamorphic exploration of an unsupervised clustering program

Sen Yang, Dave Towey, Zhi Quan Zhou

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

6 Citations (Scopus)
1 Downloads (Pure)

Abstract

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.

Original languageEnglish
Title of host publicationProceedings - 2019 IEEE/ACM 4th International Workshop on Metamorphic Testing, MET 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages48-54
Number of pages7
ISBN (Electronic)9781728122359
DOIs
Publication statusPublished - May 2019
Externally publishedYes
Event4th IEEE/ACM International Workshop on Metamorphic Testing, MET 2019 - Montreal, Canada
Duration: 26 May 2019 → …

Publication series

NameProceedings - 2019 IEEE/ACM 4th International Workshop on Metamorphic Testing, MET 2019

Conference

Conference4th IEEE/ACM International Workshop on Metamorphic Testing, MET 2019
Country/TerritoryCanada
CityMontreal
Period26/05/19 → …

Keywords

  • Clustering
  • K-means
  • Machine learning
  • Metamorphic exploration
  • Metamorphic testing
  • Unsupervised machine learning

ASJC Scopus subject areas

  • Artificial Intelligence
  • Information Systems
  • Safety, Risk, Reliability and Quality
  • Library and Information Sciences

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