Summary
This chapter introduced you to unsupervised learning algorithms, as well as how to categorize unlabeled data and identify associations between data entities. Two main areas of unsupervised learning algorithms, namely clustering and association rules, were presented. You were introduced to the most popular clustering and collaborative filtering algorithms. You were also presented with working code examples of clustering algorithms such as K-means, bisecting K-means, LDA, and GSM using code in Spark MLlib. You also saw code examples for building a recommendation engine using the alternative least-squares algorithm in Spark MLlib. Finally, a few real-world business applications of unsupervised learning algorithms were presented. We looked at several concepts, techniques, and code examples surrounding unsupervised learning algorithms so that you can train your models at scale using Spark MLlib.
So far, in this and the previous chapter, you have only explored the data wrangling...