Title :
Actionable = Cluster + Contrast?
Author :
Rahul Krishna;Tim Menzies
Author_Institution :
Comput. Sci., North Carolina State Univ., Raleigh, NC, USA
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"
Conference_Titel :
Automated Software Engineering Workshop (ASEW), 2015 30th IEEE/ACM International Conference on
DOI :
10.1109/ASEW.2015.23
