\begin{figure*}\label{fig:clusterers}
\begin{center}
\begin{tabular}{l||l|l|l|}
\cline{2-4}
& Clump & K-Means & Locally Weighted Learning \\ \hline \hline
Clusters With& Dependant attributes & Independant attributes & Independant attributes \\ \hline
Clusters By& Attribute similarity & Euclidean distance & Euclidean distance \\ \hline
Clusters on & Attribute similarity & Nearest centroid & Nearest neighboor \\ \hline
Training behavior& \begin{minipage}{120pt}Creates a decision tree with the relevant training instances at each leaf\end{minipage} & \begin{minipage}{120pt}Creates clusters to be used by another algorithm\end{minipage} & \begin{minipage}{120pt}Delays processing until testing time.\end{minipage} \\ \hline
Testing behavior& \begin{minipage}{120pt}Creates a naive bayes classifier based off of the training data at the node that the test instance resides at.\end{minipage} & \begin{minipage}{120pt}\textit{Not applicable. K-Means does no classification}\end{minipage} & \begin{minipage}{120pt}Finds the k nearest neighboors and weights them according to the normalized euclidean distance from the test instance. It then builds a naive bayes classifier off of the weighted data.\end{minipage}\\ \hline
\end{tabular}

\caption{Comparing the differnece between clustering algorithms: Clump, K-Means, and Locally Weighted Learning}
\end{center}
\end{figure*}
