Agglomerativ

Agglomerative is a term that describes a method of hierarchical clustering. In this approach, each data point begins as its own cluster. The algorithm then iteratively merges the closest pairs of clusters until only a single cluster remains, representing all the data points. This process is sometimes referred to as "bottom-up" clustering because it starts with the smallest possible clusters and builds up to larger ones.

The core of the agglomerative clustering process involves defining a similarity or distance metric between clusters. Common linkage criteria used to determine which clusters to merge include:

Single linkage: Measures the minimum distance between any two points in the respective clusters.

Complete linkage: Measures the maximum distance between any two points in the respective clusters.

Average linkage: Measures the average distance between all pairs of points in the respective clusters.

Ward's method: Merges clusters to minimize the increase in total within-cluster variance.

The output of agglomerative clustering is typically visualized as a dendrogram, a tree-like diagram that illustrates the sequence of merges and the distances at which they occurred. This dendrogram allows a user to choose the number of clusters by "cutting" the tree at a desired level. Agglomerative clustering is a versatile technique used in various fields, including biology, machine learning, and data analysis, for discovering group structures within datasets.