Machine Learning with Spark - Second Edition

K-means - interpreting cluster predictions on the MovieLens dataset

We have covered how to make predictions for a set of input vectors, but how do we evaluate how good the predictions are? We will cover performance metrics a little later; however, here, we will see how to manually inspect and interpret the cluster assignments made by our k-means model.

While unsupervised techniques have the advantage that they do not require us to provide labeled data for training, the disadvantage is that, often, the results need to be manually interpreted. Often, we would like to further examine the clusters that are found and possibly try to interpret them and assign some sort of labeling or categorization to them.

For example, we can examine the clustering ...