The remainder of this thesis is organized as follows:

\bi
	\item \chap{related} describes the related background material. This includes a description of the algorithms I will be using in experimentation. This chapter also describes some of the broad categories of classification and clustering. It details the benefits and detriments of these different approaches.
	\item \chap{clump} describes an alternative algorithm for software defect prediction called Clump. Clump is a decision tree clustering algorithm based on Ripple Down Rules(\tion{RIDOR}) by Compton\cite{rdr}.
	\item \chap{studies} presents the different experimental methods I will be exploring. It also details the datasets that are explored. Finally, it documents how relevancy filtering and clustering is explored.
	\item \chap{results} shows the results of the previously documented experiments. Here, I will show the difference in performance of global and locality based classifiers. Any discrepancies between the results shown here and prior results are explained here. 
	\item \chap{conclusions} lists the conclusions gathered. I comment on the state of locality based learning as it pertains to software defect prediction. Finally, I detail what future studies are needed to further explore locality based learning for software defect prediction.
\ei
