Our findings support the popular adage that "birds of a feather flock together." By using machine learning methods to cluster teenagers with others who have similar interests, we were able to develop a typology of teenage identities that was predictive of personal characteristics such as gender and number of friends. These same methods can be applied to other contexts with similar results.

This chapter covered only the fundamentals of clustering. There are many variants of the k-means algorithm, as well as many other clustering algorithms that bring unique biases and heuristics to the task. Based on the foundation in this chapter, you will be able to understand these clustering methods and apply them to new problems.

In the next chapter, we will begin to look at methods for measuring the success of a learning algorithm that are applicable across many machine learning tasks. While our process has always devoted some effort to evaluating the success of learning, in order to obtain the highest degree of performance, it is crucial to be able to define and measure it in the strictest terms.