The leader clustering algorithm provides a means for clustering a set of data points. Unlike many other clustering algorithms it does not require the user to specify the number of clusters, but instead requires the approximate radius of a cluster as its primary tuning parameter. The package provides a fast implementation of this algorithm in n-dimensions using Lp-distances (with special cases for p=1,2, and infinity) as well as for spatial data using the Haversine formula, which takes latitude/longitude pairs as inputs and clusters based on great circle distances.
Hartigan's clustering leader algorithm provides a means for clustering points given a predetermined radius of a cluster. Unlike other clustering algorithms it does not require the user to specify the number of clusters. The package provides a fast implementation of this algorithm in two dimensions using either euclidian distance or the Haversine formula, which takes latitude and longitude as inputs.