Hierarchical Clustering Explained

Unlike K Means, hierarchical clustering does not require you to decide the number of clusters in advance.

It builds a tree-like structure called a dendrogram, where individual data points are gradually merged into larger and larger groups based on similarity. At the bottom of this tree, every point starts as its own cluster. Moving upward, the closest clusters keep merging, until eventually everything joins into one single group at the top.

The real value of this approach is flexibility, since you can cut the dendrogram at any level to get the number of clusters that makes the most sense for your data.

This method works well for smaller datasets and is often used in biology to group genes or species based on shared characteristics.