Hierarchical clustering is an unsupervised machine learning algorithm that groups similar objects into clusters. It involves creating clusters with a predominant ordering from top to bottom, similar to how files are organized in a hierarchy on a hard disk. The algorithm starts by treating each object as an individual cluster, then iteratively merges the two closest clusters until all clusters have been merged into a single cluster representing the entire dataset. This process can be visualized using a dendrogram, which is a tree diagram that shows the hierarchical relationships between clusters.

1. Hierarchical Clustering
Machine Learning
Unit-3

2. What is Clustering ?
• Clustering is a technique that groups similar
objects such that the objects in the same group
are more similar to each other than the objects in
the other groups. The group of similar objects is
called a Cluster.

3. Hierarchical Clustering Algorithm
• HCA is an unsupervised clustering algorithm which
involves creating clusters that have predominant
ordering from top to bottom.
• For e.g: All files and folders on our hard disk are
organized in a hierarchy.
• The algorithm groups similar objects into groups
called clusters. The endpoint is a set of clusters or
groups, where each cluster is distinct from each
other cluster, and the objects within each cluster
are broadly similar to each other.

4. HCA-> Algorithm
•How does it work?
• Make each data point a single-point cluster →
forms N clusters
• Take the two closest data points and make them
one cluster → forms N-1 clusters
• Take the two closest clusters and make them one
cluster → Forms N-2 clusters.
• Repeat step-3 until you are left with only one
cluster.

5. Example:

6. There are several ways to measure the distance between clusters in
order to decide the rules for clustering, and they are often called Linkage
Methods. Some of the common linkage methods are:
• Complete-linkage: the distance between two clusters
is defined as the longest distance between two points
in each cluster.
• Single-linkage: the distance between two clusters is
defined as the shortest distance between two points in
each cluster. This linkage may be used to detect high
values in your dataset which may be outliers as they
will be merged at the end.
• Average-linkage: the distance between two clusters is
defined as the average distance between each point in
one cluster to every point in the other cluster.
• Centroid-linkage: finds the centroid of cluster 1 and
centroid of cluster 2, and then calculates the distance
between the two before merging.

7. What is Dendrogram ?
• A Dendrogram is a type of tree diagram showing
hierarchical relationships between different sets of
data.

8. Types of Hierarchical Clustering
Algorithm
1.Agglomerative Clustering- Bottom Up
2. Divisive Clustering- Top Down

11. How to draw Dendrogram

12. Thank You