  Nowadays, with the advance of technology, there is an increasing amount of
unstructured data being generated every day. However, it is a painful job to
label and organize it. Labeling is an expensive, time-consuming, and difficult
task. It is usually done manually, which collaborates with the incorporation of
noise and errors to the data. Hence, it is of great importance to developing
intelligent models that can benefit from both labeled and unlabeled data.
Currently, works on unsupervised and semi-supervised learning are still being
overshadowed by the successes of purely supervised learning. However, it is
expected that they become far more important in the longer term. This article
presents a semi-supervised model, called Batch Semi-Supervised Self-Organizing
Map (Batch SS-SOM), which is an extension of a SOM incorporating some advances
that came with the rise of Deep Learning, such as batch training. The results
show that Batch SS-SOM is a good option for semi-supervised classification and
clustering. It performs well in terms of accuracy and clustering error, even
with a small number of labeled samples, as well as when presented to
unsupervised data, and shows competitive results in transfer learning scenarios
in traditional image classification benchmark datasets.
