3. Unsupervised Learning

Unsupervised methods are based on data sets that do not contain labels. This means that the algorithms are learning only using feature vectors. This group of learning methods is also known under different names. It depends on the context where it is used. Unsupervised learning can be called learning without a teacher. It is the opposite to learning with a teacher, supervised learning. Unsupervised learning is also known as partitioning, segmentation, typology, numerical taxonomy, or clustering. The last term is one of the most commonly used, aside from unsupervised learning. A cluster is a set of elements/objects of the same label. Compared to supervised methods, the label used here is based on similarities between elements of each cluster. It means that some elements are more similar to other elements than to other elements. In other words, the goal of the clustering method is to find groups of objects that are most similar to each other. It is important to mention that if we say label in the context of unsupervised learning, we mean the testing part of a method. Labels are assigned during the learning phase. Each element/object belongs to a group. Each group has its own label that is different for each group.