This thesis makes many contributions to literature including:
\bi
\item A new decision tree based clustering algorithm called Clump. By augmenting it with a Naive Bayes classifier, it functions as a local classifier.
\bi
\item This algorithm is designed to solve the "Why?" problem with a standard Naive Bayes classifier.
\ei
\item A review of standard clustering and classification algorithms. This includes their design and usage.
\item An in-depth look at the state of locality based learning when applied to software defect prediction.
\item A discussion on the impact of various classification and clustering algorithms versus the impact of the data used.
\ei
