Current clustering algorithms are slow, with high order polynomial
run-times. Current algorithms also cluster data by Euclidean
distance. We propose an algorithm called Clump. Clump is a Naive Bayes
classifier augmented by a rule tree based clusterer. Clump runs in low
order polynomial time, and clusters by attribute similarity rather
than full Euclidean distance. Clump clusters the data, and during
testing, forms a naive bayes classifier with the data at each node.

Clump and other learners are tested against 7 datasets. These
datasets\cite{PromiseData} represent projects ranging from video
guidance, a NASA dataset, to small appliance controllers, a SoftLab dataset.

\textbf{Information about the results and conclusion will go here later.}
