• DocumentCode
    3758037
  • Title

    Actionable = Cluster + Contrast?

  • Author

    Rahul Krishna;Tim Menzies

  • Author_Institution
    Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
  • fYear
    2015
  • Firstpage
    14
  • Lastpage
    17
  • Abstract
    There are many algorithms for data classification such as C4.5, Naive Bayes, etc. Are these enough for learning actionable analytics? Or should we be supporting another kind of reasoning? This paper explores two approaches for learning minimal, yet effective, changes to software project artifacts.
  • Keywords
    "Decision trees","Clustering algorithms","Data mining","Business","Clustering methods","Planning","Stability analysis"
  • Publisher
    ieee
  • Conference_Titel
    Automated Software Engineering Workshop (ASEW), 2015 30th IEEE/ACM International Conference on
  • Type

    conf

  • DOI
    10.1109/ASEW.2015.23
  • Filename
    7426630
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3758037