As usual, you'll use the following libraries:

• sklearn for generating dummy data and implementing hierarchical clustering (AgglomerativeClustering);

• scipy for generating and working with the dendrogram;

• matplotlib for visualizing the clusters and the dendrogram;

• numpy for numerical operations.

Generating Dummy Data

You can use the make_blobs() function from scikit-learn to generate datasets with different numbers of clusters and varying degrees of separation. This will help you see how hierarchical clustering performs in different scenarios.

The general algorithm is as follows:

1. You instantiate the AgglomerativeClustering object, specifying the linkage method and other parameters;

2. You fit the model to your data;

3. You can extract cluster labels if you decide on a specific number of clusters;

4. You visualize the clusters (if the data is 2D or 3D) using scatter plots;

5. You use SciPy's linkage to create the linkage matrix and then dendrogram to visualize the dendrogram.

You can also experiment with different linkage methods (e.g., single, complete, average, Ward's) and observe how they affect the clustering results and the dendrogram's structure.