Hierarchical Clustering

Hierarchical clustering is a collection of algorithms that form nested clusters by continuously merging from bottom to top or continuously splitting from top to bottom. This hierarchical class is represented by a "dendrogram", and the Agglomerative Clustering algorithm is one of them.

Hierarchical clustering attempts to divide sample data at different "levels" and cluster them layer by layer. In the clustering tree, the original data points of different categories are at the bottom of the tree, and the root node of a cluster is the top layer of the tree.

Classification of Hierarchical Clustering

There are currently two methods for creating clustering trees: bottom-up merging and top-down splitting.

The merging algorithm of hierarchical clustering mainly calculates the similarity between two types of data points, and combines the two most similar data points among all data points, and iterates repeatedly. In simple terms, the algorithm determines the similarity by calculating the distance between data points. The smaller the distance, the higher the similarity. It also combines the two data points or categories with the closest distance to generate a clustering tree.