Hierarchical clustering is a technique of cluster evaluation that builds a hierarchy of clusters. There are two essential varieties of hierarchical clustering: Agglomerative (bottom-up) and Divisive (top-down). On this instance, we’ll concentrate on Agglomerative Hierarchical Clustering, which begins by treating every knowledge level as a single cluster after which iteratively merges the closest pairs of clusters till all knowledge factors are in a single cluster.
Let’s take into account the next dataset of 5 factors in 2D house:
Knowledge Factors: A(1,1),B(2,1),C(4,3),D(5,4),E(6,5)
Learn right here for full article:
https://eevibes.com/blog/hierarchical-clustering-with-example/
