Clustering Refresher

Chapter 1, Introduction to Clustering, covered both the high-level concepts and in-depth details of one of the most basic clustering algorithms: k-means. While it is indeed a simple approach, do not discredit it; it will be a valuable addition to your toolkit as you continue your exploration of the unsupervised learning world. In many real-world use cases, companies experience valuable discoveries through the simplest methods, such as k-means or linear regression (for supervised learning). An example of this is evaluating a large selection of customer data – if you were to evaluate it directly in a table, it would be unlikely that you'd find anything helpful. However, even a simple clustering algorithm can identify where groups within the data are similar and dissimilar. As a refresher, let's quickly walk through what clusters are and how k-means works to find them:

Figure 2.1: The attributes that separate supervised and unsupervised...