I will also explore the Burak Filter as a relevancy filter. The Burak Filter is a modified K Nearest Neighbor algorithm used to filter the training data to only include the training instances which are similar to the testing instances. I will compare the performance of each algorithms listed in \tion{locallearners}, \tion{nonlocallearners}, and \chap{clump} against the algorithm augmented by the Burak Filter.

With this, I hope to show that the Burak Filter, currently the "gold standard" in relevancy filtering, does not beneficially impact the performance of the clustering algorithms listed above. 
