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.
|Author||Taylor B. Arnold|
|Date of publication||2014-12-16 00:58:11|
|Maintainer||Taylor B. Arnold <[email protected]>|
|Package repository||View on CRAN|
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