\begin{slide}[method=file,toc=Separation]{What Separates the Clusters?}
\twocolumn[lcolwidth=2.5in,rcolwidth=1.5in]{
\begin{raggedleft}
{\tiny
DT3= use of defect tracking

CL3= CMM level

US3= use of standards

EX3= experience

HS2= human safety

}

~\\

\bi
\item ``Tag'' each record with its cluster identifier
\item Using feature subset selection, learn which SILAP attributes
are most important
\bi
\item The CFS algorithm (in a 10-way cross-val) selected seven: 
{\color{red} HS2, EX3, US3, CL3, FR3, DT3, RM3}
\ei
\item Learn a decision procedure that identifies  each clusters
\item If SILAP performs differently for each cluster, then
those clusters represent truly different project types.
\ei

\end{raggedleft}}{
\begin{verbatim}
DT3 <= 1: cluster2 (150.0/4.0)\newline
DT3 > 1\newline
|   CL3 <= 4\newline
|   |   US3 <= 2: cluster0 (170.0)\newline
|   |   US3 > 2\newline
|   |   |   DT3 <= 2\newline
|   |   |   |   EX3 <= 2: cluster3 (25.0)\newline
|   |   |   |   EX3 > 2\newline
|   |   |   |   |   HS2 <= 3: cluster0 (14.0)\newline
|   |   |   |   |   HS2 > 3: cluster3 (6.0)\newline
|   |   |   DT3 > 2: cluster0 (42.0)\newline
|   CL3 > 4: cluster1 (19.0)
\end{verbatim}
~\\~\\
In a 10-way cross-val, accuracy=99.061\% (!!).
~\\~\\
``The variables in the tree are all about how much a project knows
about itself and how much it is willing to share that knowledge
with others.''
}
\end{slide}
