Machine Learning with R, the tidyverse, and mlr

Chapter 19. Clustering based on distributions with mixture modeling

This chapter covers

• Understanding mixture model clustering
• Understanding the difference between hard and soft clustering

Our final stop in unsupervised learning techniques brings us to an additional approach to finding clusters in data: mixture model clustering. Just like the other clustering algorithms we’ve covered, mixture model clustering aims to partition a dataset into a finite set of clusters.

In chapter 18, I showed you the DBSCAN and OPTICS algorithms, and how they find clusters by learning regions of high and low density in the feature space. Mixture model clustering takes yet another approach to identify clusters. A mixture model is any model that describes a dataset ...