  In the recent years, there is a growing interest in semi-supervised learning,
since, in many learning tasks, there is a plentiful supply of unlabeled data,
but insufficient labeled ones. Hence, Semi-Supervised learning models can
benefit from both types of data to improve the obtained performance. Also, it
is important to develop methods that are easy to parameterize in a way that is
robust to the different characteristics of the data at hand. This article
presents a new method based on Self-Organizing Map (SOM) for clustering and
classification, called Adaptive Local Thresholds Semi-Supervised
Self-Organizing Map (ALTSS-SOM). It can dynamically switch between two forms of
learning at training time, according to the availability of labels, as in
previous models, and can automatically adjust itself to the local variance
observed in each data cluster. The results show that the ALTSS-SOM surpass the
performance of other semi-supervised methods in terms of classification, and
other pure clustering methods when there are no labels available, being also
less sensitive than previous methods to the parameters values.
