Klusterihierarkia

Klusterihierarkia, also known as hierarchical clustering, is a method of cluster analysis that seeks to build a hierarchy of clusters. This hierarchy can be visualized as a dendrogram, a tree-like diagram that illustrates the arrangement of the clusters produced by the algorithm. There are two main approaches to hierarchical clustering: agglomerative (bottom-up) and divisive (top-down).

Agglomerative clustering starts with each data point as its own cluster. It then iteratively merges the two closest clusters until only one cluster remains. The decision of which clusters to merge at each step depends on a chosen linkage criterion, such as single linkage, complete linkage, or average linkage. Single linkage considers the minimum distance between any two points in the clusters, complete linkage considers the maximum distance, and average linkage considers the average distance between all pairs of points.

Divisive clustering, on the other hand, begins with a single cluster containing all data points. It then recursively splits clusters into smaller ones until each data point is in its own cluster. While less common than agglomerative clustering, it serves the same purpose of creating a hierarchical structure.

The output of hierarchical clustering, the dendrogram, allows for the interpretation of relationships between data points at different levels of granularity. A "cut" can be made at a desired level in the dendrogram to obtain a specific number of clusters. This flexibility makes hierarchical clustering useful in various fields, including biology, finance, and information retrieval, for tasks like taxonomy generation and document organization.