This chapter has discussed a variety of classification algorithms, as well as clustering and indexing algorithms. The algorithms discussed present industry accepted methods for software defect prediction. I covered both locality based, and non-locality based classification algorithms. Each of these algorithms operate in a different fashion. The different algorithms use: \bd
\item[Naive Bayes] Frequency counts
\item[Ridor] A decision tree
\item[OneR] A group of rules
\item[RIPPER] An inductive rule learner
\item[LWL] A locally weighted Naive Bayes classifier
\item[Clump] A decision tree clustering algorithm augmented by Naive Bayes
\item[C4.5] A information entropy based decision tree
\ed

This chapter also includes many clustering and indexing algorithms such as K-Means, GAC, EM, Ball Trees, KD-Trees and Cover Trees. In future chapters, I will explore the effect of  K-Means and Greedy Agglomerative Clustering on the above classifiers to further explore the effects of locality in software defect prediction.

The next chapter details a proposed algorithm for software defect prediction called Clump.
