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\title{IDEA: Iterative Dichotomization on Every Attribute}

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\section{Todo List}
This is a \emph{rough} timeline taking dependencies into consideration.

\begin{itemize}

\item Cosmetics 
\begin{itemize} 
	\item \sout{Thinner lines for block outlines.}
	\item \sout{Use a color scale to represent cluster scores.}
	\item \sout{Graph legend showing colors with scores.}
	\item Only display clustered quadrants.
	\item Try only showing the top 80\% densest clusters.
\end{itemize}

\item Performance Scores
\begin{itemize}
	\item \sout{Eliminate self testing by setting aside train/test sets.}
\end{itemize}

\item Clustering
\begin{itemize}
	\item Takeaways from the Teak Experiment \cite {07teak}
	\begin{itemize}
		\item \sout{Stop splitting if the variance increases, lives=3.}
	\end{itemize}
	\item Mark (gray out) clusters which have a large delta between their performance scores (neighboring clusters which have high/low scores.)
	\item Instead of using a 10\% similarity rule in the GRIDCLUS algorithm \cite{93Schikuta}, find the largest drop in block similarity and use that as a basis for similar.
\end{itemize}

\item Competency
\begin{itemize}
	\item Compare performance for logged vs. unlogged coordinates.
	\item Compare performance for pruned vs. unpruned with lives=3 system.  Performance should increase once we prune.
\end{itemize}

\item Interactive Clustering
\begin{itemize}
	\item Import data.
	\item Assign attributes.
	\item Show statistics for active cluster / all clusters.
\end{itemize}

\item Feature Subset Selection
\begin{itemize}
	\item What's best among the best clusters?
\end{itemize}

\item Contrast Sets
\begin{itemize}
	\item What makes cluster X different from unclustered or neighboring clusters?
\end{itemize}

\item Clean up and profile code.

\end{itemize}

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