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Connectivity-based clustering is also called hierarchical clustering because it creates clusters in a hierarchy.

In clustering, data points closer to each other show more similarity than those farther from each other.

The algorithm begins by assigning data points to a cluster of their own.

Then the two closest clusters are merged to form a single cluster.

In the end, the algorithm ends with only one cluster remaining.

There are two scenarios for this model:

In the first, data points are classified into distinct groups and then aggregated as the distance between them decreases.

In the second, the data points are distributed into a single large cluster and then separated as the distance between them increases.