Clustering Refresher
Chapter 1, Introduction to Clustering, covered both the high-level intuition 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 groundbreaking discoveries through the simplest methods, such as k-means or linear regression (for supervised learning). 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 problems
If you were given a random collection of data without any guidance, you would likely start your exploration using basic statistics – for example, what the mean, median, and mode values are of each of the features. Remember that, from a high-level data model that simply exists, knowing whether it is supervised...
