In this section, two clustering algorithms will be explored: Greedy Agglomerative Clustering and K-Means. These two clusterers are applied to eight Within-Company datasets: AR3, AR4, AR5, CM1, KC1, KC2, KC3, MW1, and MC2. PC1 was omitted from this experiment because of the number of instances contained in it. Greedy Agglomerative Clustering runs in $O(n^2)$ time, and made running it on PC1 time prohibitive. For this same reason Cross-Company datasets are also omitted. PC1 and Cross-Company are also omitted from the experiment on K-Means to maintain an even ground. The datasets used in this section are the modified datasets used by Turhan et al.\cite{08Turhan}. AR3, AR4, and AR5 each share 29 common attributes. The remaining 6 datasets share 19 common attributes. These are shown in \fig{attribute-clustering}.

\input{figures/clustererclassifier}
The results for K-Means and Greedy Agglomerative Clustering are generated by augmenting the clustering algorithms with a standard Naive Bayes classifier as shown in \fig{clusterer+classifier}. Each clustering algorithm is trained using the standard training set. Next, the testing instances are assigned to the nearest\footnote{The nearest cluster is determined by an algorithm specific function} cluster. Finally an instance of Naive Bayes is trained for each cluster using the training data assigned to the cluster. Each instance instance in the training set is assigned to the nearest cluster, and classified using the Naive Bayes model for the nearest cluster.

