Classification trees are used with a categorical response variable. The goal of a classification tree is to derive a model that predicts to which category a particular subject or individual belongs, based one or more explanatory factors. For example, we could use a classification tree to predict the diagnosis (Benign or Malignant) of a particular patient based upon information obtained by doctors through scanned images. These classification trees are displayed as a decision tree that has a start node which then branches into other nodes.

When presented with a dataset, it is beneficial to identify any relationships or trends. One way in which we can accomplish this is through the application of cluster analysis, a method for developing taxonomies within a set of observations. While this technique is beneficial in marketing, research, or any profession requiring data analysis, there are many algorithms for dfining clusters in a dataset. As a result, we raise the question, which clustering algorithm is the best in various scenarios?