5 Clustering
Out of complexity, find simplicity.
Sometimes life is very simple, and sometimes we experience quite complex situations. We sail through both situations and change our approach as needed.
In part 1, we covered the fundamentals to prepare you for the journey ahead. We are now in part 2, which is slightly more complex than part 1. Part 3 will be more advanced than the first two parts. So please give careful attention to the coming chapters, as the skills and knowledge gained here will prepare you for the later chapters in the book.
Before starting this chapter, we should refresh our memory on what we covered in chapter 2. We studied clustering algorithms in part 1 of the book. In chapter 2, we learned that clustering is an unsupervised learning technique where we wish to group the data points by discovering interesting patterns in the datasets. We went through the meaning of clustering solutions and different categories of clustering algorithms and looked at a case study. In that chapter, we explored k-means clustering, hierarchical clustering, and DBSCAN clustering in depth. We went through the mathematical background, process, and Python implementation and the pros and cons of each algorithm.