Two groups of classification algorithms are explored. The first group are locality based classification algorithms such as LWL, Ridor, Clump, and RIPPER. The second group are non-locality based learners such as C4.5, OneR, and Naive Bayes. I will explore the difference in performance of the locality based and non-locality based classifiers. In these results, I will also compare the difference in using Within Company data versus Cross Company data. Finally, I will explore an effect demonstrated by Turhan\cite{08Turhan} where logging the numerical data helps improve the classification performance.\label{pg:confusingLine}

\input{tables/configOptions}
Each of the classifiers have various configuration options described in \tbl{configOptions}. \tbl{configOptions} also details the options which were chosen for experimentation. Multiple versions of LWL were tried, using a range of $K$ values.
