In this section, we'll look at the differences between unsupervised learning and semi-supervised learning.
Unsupervised learning develops a model based on unlabeled data, whereas semi-supervised learning employs both labeled and unlabeled data.
We use expected maximization, hierarchical clustering, and k-means clustering algorithms in unsupervised learning, whereas in semi-supervised learning, we apply either active learning or bootstrapping algorithms.
In Weka, we can perform semi-supervised learning using the collective-classification package. We will look at installing the collective-classification package later in this chapter, and you'll see how you can perform semi-supervised learning using collective classification.