  There has been an increasing interest in semi-supervised learning in the
recent years because of the great number of datasets with a large number of
unlabeled data but only a few labeled samples. Semi-supervised learning
algorithms can work with both types of data, combining them to obtain better
performance for both clustering and classification. Also, these datasets
commonly have a high number of dimensions. This article presents a new
semi-supervised method based on self-organizing maps (SOMs) for clustering and
classification, called Semi-Supervised Self-Organizing Map (SS-SOM). The method
can dynamically switch between supervised and unsupervised learning during the
training according to the availability of the class labels for each pattern.
Our results show that the SS-SOM outperforms other semi-supervised methods in
conditions in which there is a low amount of labeled samples, also achieving
good results when all samples are labeled.
