5 Clustering (advanced)
“Out of complexity, find simplicity– Einstein”
Sometimes life is very simple, and sometimes we experience quite complex situations. We sail through both the situations and change our approach as per the situation.
In the Part one of the book we covered easier and simpler topics. It made you ready for the journey ahead. We are currently in Part two which is slightly more complex than Part one. Part three is more advanced than the first two parts. So, the level of difficulty will increase slightly with each and every chapter along with the expectations.
We studied clustering algorithms in part one of the book. We understand 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, different categories of clustering algorithm and a case study at the end. In that chapter, we explored kmeans clustering, hierarchical clustering and DBSCAN clustering in depth. We went through the mathematical background, process, Python implementation and pros and cons of each. Before starting this chapter, it is advisable to refresh chapter two.