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What is hierarchical clustering? Explain any two techniques for finding distance between the clusters in hierarchical clustering
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## Clustering

• Clustering is one of the unsupervised Machine Learning mechanisms that search for similarity and relationship patterns among data samples and then creates a cluster of those samples based on similarity measurements.
• This clustering mechanism is further classified into various types, such as Density-based, Hierarchical-based, Partitioning, and Grid.

## Hierarchical Clustering

• This type of clustering groups together the unlabeled data points having similar characteristics.
• Hierarchical clustering treats every data point as a separate cluster.
• Then, it repeatedly executes the subsequent steps like, Identify the two clusters which can be closest together, and merging the two maximum comparable clusters.
• This process needs to continue until all the clusters are merged.
• Hence, this method creates a hierarchical decomposition of the given set of data objects.
• Based on this how the hierarchical decomposition is formed this clustering is further classified into two types,
• Agglomerative Approach
• Divisive Approach
• Hierarchical clustering typically works by sequentially merging similar clusters. This is known as agglomerative hierarchical clustering.
• In theory, it can also be done by initially grouping all the observations into one cluster, and then successively splitting these clusters. This is known as divisive hierarchical clustering.
• Divisive clustering is rarely done in practice.

# Agglomerative Approach

• This approach is also known as the Bottom-Up Approach.
• This approach starts with each object forming a separate group.
• It keeps on merging the objects or groups that are close to one another.
• It keeps on doing so until all of the groups are merged into one or until the termination condition holds.
• Algorithm for Agglomerative Hierarchical Clustering is:
• Step 1 - Calculate the similarity of one cluster with all the other clusters. Calculation of Proximity Matrix.
• Step 2 - Consider every data point as an individual cluster.
• Step 3 - Merge the clusters which are highly similar or close to each other.
• Step 4 - Recalculate the proximity matrix for each cluster.
• Step 5 - Repeat Steps 3 and 4 until only a single cluster remains.

## Divisive Approach

• This approach is also known as the Top-Down Approach.
• This approach starts with all of the objects in the same cluster.
• In the continuous iteration, a cluster is split up into smaller clusters.
• It is down until each object in one cluster or the termination condition holds.
• This method is rigid, i.e., once a merging or splitting is done, it can never be undone.