In this chapter, the results of the experiments described in \chap{studies} are listed and examined. First, various classifiers are examined looking closely at the difference in performance between the locality based classifiers and the non-locality based classifiers. Second, the effect described by Turhan et al.\cite{08Turhan} is looked when using the reporting format described in \tion{evaluation}. Third, two clustering algorithms(K-Means and Greed Agglomerative Clustering) will be compared against Naive Bayes. The results of each of these studies will be displayed and analyzed. These three studies represent very different applications of locality during the classification process:
\bi
\item Within the classifier,
\item Filtering the training dataset according to the testing set, and
\item Creating a training dataset for each testing instance.
\ei
After the results for each section are reviewed, they are compared to the results one would expect if locality holds true.
