Microarray experiments offer the user the potential to monitor gene expression across thousands of genes at once. However, researchers are often left with a dimensionality problem - too few technological and biological replicates, and thousands of genes to monitor for differential expression. The problem of finding interesting and novel genes within the thousands of genes on a microarray can seem akin to looking for a needle in a haystack of needles. One highly popular approach to finding interesting genes for further study is finding similar patterns of expression within the data. A common hypothesis-generating approach, clustering, has been shown to have much potential in finding genes with similar functions. However, there are a variety of clustering methods, and each has different strengths and weaknesses in finding patterns within microarray data.