A large part of data analysis is looking for patterns in our dataset. Of course, a dataset of any complexity is too much for the human mind to see patterns in, so we rely on computers, statistics, and machine learning to augment our insights.

In this chapter, we'll take a look at a number of methods for clustering and classifying data. Depending on the nature of the data and the question we're trying to answer, different algorithms will be more or less useful. For instance, K-means clustering is great for clustering numeric datasets, it's poorly suited for working with nominal data.

Most of the recipes in this chapter will use the Weka machine learning and data mining library (http://www.cs.waikato.ac.nz/ml/weka/). This is a full-featured library for analyzing data using many different procedures and algorithms. It includes a more complete set of these algorithms than Incanter, which we've been using a lot so far. We'll start by seeing how to load CSV files into Weka and work...