Hierarchical Clustering
Dependencies
Introduction
In statistics, hierarchical clustering (also called hierarchical cluster analysis or HCA) is a method of cluster analysis which seeks to build a hierarchy of clusters. Strategies for hierarchical clustering generally fall into two types:
-Agglomerative: This is a “bottom-up” approach: each observation starts in its own cluster, and pairs of clusters are merged as one moves up the hierarchy.
-Divisive: This is a “top-down” approach: all observations start in one cluster, and splits are performed recursively as one moves down the hierarchy.
In general, the merges and splits are determined in a greedy manner. The results of hierarchical clustering are usually presented in a dendrogram.
In the Notebook, we learn how to use scipy and scikit-learn to implement Agglomerative hierarchical clustering which is more popular than the Divisive one.