The divisive clustering algorithm is a top-down clustering approach in which all points in the dataset are initially assigned to one cluster and then split iteratively as one progresses down the hierarchy.
The technique of putting related elements together is known as "clustering." The purpose of this unsupervised machine learning technique is to look for commonalities in data points and group them together.
Clustering is the most significant unsupervised learning problem, and it deals with identifying a structure in a collection of unlabeled data, much like any other issue of this type. "The process of grouping objects into groups whose members are related in some way," according to a broad definition of clustering.