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 teen identities that was predictive of personal characteristics like gender and the number of friends. These same methods can be applied to other contexts with similar results.

This chapter covered only the fundamentals of clustering. As a very mature machine learning method, there are a myriad of variants to the k-means algorithm as well as many alternatives which bring unique biases and heuristics to the task. Based on what you have learned here, you will be able to understand and apply other clustering methods 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.