Summary

In this chapter, we have explored what clustering is and why it is important in a variety of data challenges. Building upon this foundation of clustering knowledge, you implemented k-means, which is one of the simplest yet most popular methods of unsupervised learning. If you have reached this summary and can repeat what k-means does step-by-step to your fellow classmate, good job! If not, please go back and review the previous material – the content only grows in complexity from here. From here, we will be moving on to hierarchical clustering, which, in one configuration, reuses the centroid learning approach that we used in k-means. We will build upon this approach by outlining additional clustering methodologies and approaches in the next chapter.