klusterihierarkian

Klusterihierarkia, also known as hierarchical clustering, is a method of cluster analysis that seeks to build a hierarchy of clusters. This hierarchy can be represented as a tree-like structure called a dendrogram. There are two main approaches to hierarchical clustering: agglomerative and divisive.

Agglomerative clustering, also known as bottom-up clustering, begins with each data point as its own cluster. The algorithm then iteratively merges the closest pair of clusters until only one cluster remains, containing all data points. The "closeness" of clusters is determined by a linkage criterion, such as single linkage (minimum distance between points in different clusters), complete linkage (maximum distance), or average linkage (average distance).

Divisive clustering, also known as top-down clustering, starts with all data points in a single cluster. The algorithm then recursively splits clusters into smaller clusters until each data point is in its own cluster, or a stopping criterion is met. While conceptually the inverse of agglomerative clustering, divisive methods are generally more computationally intensive.

The resulting dendrogram from hierarchical clustering can be cut at different levels to obtain a varying number of clusters. This provides flexibility in determining the optimal number of clusters for a given dataset. Hierarchical clustering is useful for exploring data structures and understanding relationships between data points without pre-specifying the number of clusters. However, it can be computationally expensive for large datasets.