Humans typically tend to agglomerate everyday elements into groups of similar features. This feature of the human mind can also be replicated by an algorithm. Conversely, one of the simplest operations that can be initially applied to any unlabeled dataset is to group elements around common features.
As we have described, in this stage of the development of the discipline, clustering is taught as an introductory theme that's applied to the simplest categories of element sets.
But as an author, I recommend researching this domain, because the community is hinting that the current model's performance will all reach a plateau, before aiming for the full generalization of tasks in AI. And what kinds of method are the main candidates for the next stages of crossing the frontier towards AI? Unsupervised methods, in the form of very sophisticated...
