In this chapter, we learned about the concept of segmentation and its association with clustering, an ML unsupervised learning technique. We made use of the wholesale dataset available from the UCI repository and implemented clustering using the k-means, DIANA, and AGNES algorithms. During the course of this chapter, we also studied various aspects related to clustering, such as tendency to cluster, distance, linkage measures, and methods to identify the right number of clusters, and measuring the output of clustering. We also explored making use of the clustering output for customer-segmentation purposes.
Can computers see and identify objects and living creatures like humans do? Let's explore the answer to this question in the next chapter.