Give brief introduction to Hierarchical Clustering?

Data Science, AI and ML
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Hierarchical Clustering creates clusters in a hierarchical tree-like structure (also called a Dendrogram). Meaning, a subset of similar data is created in a tree-like structure in which the root node corresponds to the entire data, and branches are created from the root node to form several clusters.

A Hierarchical clustering method works via grouping data into a tree of clusters. Hierarchical clustering begins by treating every data point as a separate cluster. Then, it repeatedly executes the subsequent steps:

  1. Identify the 2 clusters which can be closest together, and
  2. Merge the 2 maximum comparable clusters. We need to continue these steps until all the clusters are merged together.

In Hierarchical Clustering, the aim is to produce a hierarchical series of nested clusters. A diagram called Dendrogram (A Dendrogram is a tree-like diagram that statistics the sequences of merges or splits) graphically represents this hierarchy and is an inverted tree that describes the order in which factors are merged (bottom-up view) or clusters are broken up (top-down view).

The basic method to generate hierarchical clustering is

1. Agglomerative: Initially consider every data point as an individual Cluster and at every step, merge the nearest pairs of the cluster. (It is a bottom-up method). At first, every dataset is considered as an individual entity or cluster. At every iteration, the clusters merge with different clusters until one cluster is formed.

2. Divisive: We can say that Divisive Hierarchical clustering is precisely the opposite of Agglomerative Hierarchical clustering. In Divisive Hierarchical clustering, we take into account all of the data points as a single cluster and in every iteration, we separate the data points from the clusters which aren’t comparable. In the end, we are left with N clusters.