Understanding hierarchical clustering
Hierarchical clustering is a method of clustering that creates a hierarchy or tree-like structure of clusters. It iteratively merges or splits clusters based on the similarity or dissimilarity between data points. The resulting structure is often represented as a dendrogram, which visualizes the relationships and similarities among the data points.
There are two main types of hierarchical clustering:
- Agglomerative hierarchical clustering: This starts with each data point considered as an individual cluster and progressively merges similar clusters until all data points belong to a single cluster. At the beginning, each data point is treated as a separate cluster, and in each iteration, the two most similar clusters are merged into a larger cluster. This process continues until all data points are in one cluster. The merging process is guided by a distance or similarity measure, such as a Euclidean distance or correlation.
- Divisive...
