Part 3. Unsupervised learning

This third part of Machine Learning in Action deals with unsupervised learning. This is a break from what was covered in the first two sections. In unsupervised learning we don’t have a target variable as we did in classification and regression. Instead of telling the machine “Predict Y for our data X,” we’re asking “What can you tell me about X?” Things we ask the machine to tell us about X may be “What are the six best groups we can make out of X?” or “What three features occur together most frequently in X?”

We start our study of unsupervised learning by discussing clustering (grouping similar items together) and the k-means clustering algorithm in chapter 10. Next, we look into association analysis or shopping basket analysis with the Apriori algorithm in chapter 11. Association analysis can help us answer the question “What items are mostly commonly bought together?” We finish our study of unsupervised learning in chapter 12 with a more efficient algorithm for association analysis: the FP-growth algorithm.