Summary

Machine learning-based clustering techniques are great in that they help speed up the segmentation process and can find patterns in data that can escape highly proficient analysts. Multiple techniques for clustering have been developed over the decades, each having its merits and drawbacks. As a data science practitioner in marketing, understanding different techniques will make you far more effective in your practice. However, faced with multiple options in techniques and hyper-parameters, it's important to be able to compare the results from the techniques objectively. This, in turn, requires you to quantify the quality of clusters resulting from a clustering process.

In this chapter, you learned various methods for choosing the number of clusters, including judgment-based methods such as visual inspection of cluster overlap and elbow determination using the sum of squared errors/ inertia, and objective methods such as evaluating the silhouette score. Each of these...