I will explore clustering algorithms by applying K-Means and Greedy Agglomerative Clustering to the classifiers listed above. The performance of these clustering algorithms and the Burak Filter are compared by applying them to the datasets in \tion{datasets}\footnote{AR3, AR4, AR5, CM1, KC1, KC2, KC3, MC2, MW1} and the locality based classifiers listed in \tion{locallearners}, non-locality based classifiers in \tion{nonlocallearners}\footnote{Local classifiers: LWL, Ridor, RIPPER. Non-Local Classifiers: C4.5, Naive Bayes, OneR}, and Clump.

With these experiments, I hope to show that locality by clustering does not beneficially impact the performance of the classification algorithms.
