Single linkage hierarchical clustering

Single linkage hierarchical clustering is a method used in cluster analysis to group similar data points into clusters based on their proximity or similarity. It is a bottom-up approach, starting with each data point as its own cluster and then iteratively merging the closest pairs of clusters until only one cluster remains.

In single linkage clustering, the distance between two clusters is defined as the minimum distance between any two points in the two clusters. So, the distance between two clusters is determined by the closest points in each cluster. This method tends to produce elongated clusters because it prioritizes the linkage between the closest points, often leading to what is known as the “chaining effect”.

Single linkage clustering is relatively easy to implement and computationally efficient, making it suitable for large datasets. However, it can be sensitive to noise and outliers, and it may produce clusters of varying sizes and shapes.