This chapter detailed a new algorithm that was a merger between a clusterer and a classifier called Clump. The clustering aspect of Clump differs from other clustering algorithms by creating its clusters with a decision tree. Clump relies on individual attribute values, and not a standard Euclidean distance. The classification aspect of Clump uses Naive Bayes. Clump performs similarly to Naive Bayes, but offers an advantage over Naive Bayes by providing an explanation for its decisions by way of the decision trees. Clump also has a higher probability of detection than the other classifiers. These results are detailed in \chap{results}.

The next chapter details the experimental design, the pre-processing algorithms used, and the methods used for 
analysis.
