Machine Learning with Spark - Second Edition

Building a Clustering Model with Spark

In the last few chapters, we covered supervised learning methods, where the training data is labeled with the true outcome that we would like to predict (for example, a rating for recommendations and class assignment for classification or a real target variable in the case of regression).

Next, we will consider the case where we do not have labeled data available. This is called unsupervised learning, as the model is not supervised with the true target label. The unsupervised case is very common in practice, since obtaining labeled training data can be very difficult or expensive in many real-world scenarios (for example, having humans label training data with class labels for classification). However, ...