Clustering is based on the concepts of similarity and distance, while proximity is determined by a distance function. It allows the generation of clusters where each of these groups consists of individuals who have common features with each other.
Overall, the analysis of clusters is similar to the classification models, with the difference that the groups are not preset. The goal is to perform a partition of data into clusters that can be disjoint or not.
An important point in clustering techniques is that the groups are not given a priori and this implies that the person doing the analysis should support the interpretation of the groups found.
There are many methods, and the most popular are based on Hierarchical ...